Understanding fish movement trajectories in aquaculture is essential for practical applications,such as disease warning,feeding optimization,and breeding management.These trajectories reveal key information about the ...Understanding fish movement trajectories in aquaculture is essential for practical applications,such as disease warning,feeding optimization,and breeding management.These trajectories reveal key information about the fish’s behavior,health,and environmental adaptability.However,when multi-object tracking(MOT)algorithms are applied to the high-density aquaculture environment,occlusion and overlapping among fish may result in missed detections,false detections,and identity switching problems,which limit the tracking accuracy.To address these issues,this paper proposes FishTracker,a MOT algorithm,by utilizing a Tracking-by-Detection framework.First,the neck part of the YOLOv8 model is enhanced by introducing a Multi-Scale Dilated Attention(MSDA)module to improve object localization and classification confidence.Second,an Adaptive Kalman Filter(AKF)is employed in the tracking phase to dynamically adjust motion prediction parameters,thereby overcoming target adhesion and nonlinear motion in complex scenarios.Experimental results show that FishTracker achieves a multi-object tracking accuracy(MOTA)of 93.22% and 87.24% in bright and dark illumination conditions,respectively.Further validation in a real aquaculture scenario reveal that FishTracker achieves aMOTA of 76.70%,which is 5.34% higher than the baselinemodel.The higher order tracking accuracy(HOTA)reaches 50.5%,which is 3.4% higher than the benchmark.In conclusion,FishTracker can provide reliable technical support for accurate tracking and behavioral analysis of high-density fish populations.展开更多
To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework ba...To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.展开更多
To cope with multi-object tracking under real-world complex situations, a new video-based method is proposed. In the detecting step, the moving objects are segmented with the third level DWT （discrete wavelet transfo...To cope with multi-object tracking under real-world complex situations, a new video-based method is proposed. In the detecting step, the moving objects are segmented with the third level DWT （discrete wavelet transform ）and background difference. In the tracking step, the Kalman filter and scale parameter are used first to estimate the object position and bounding box. Then, the center-association-based projection ratio and region-association-based occlusion ratio are defined and combined to judge object behaviours. Finally, the tracking scheme and Kalman parameters are adaptively adjusted according to object behaviour. Under occlusion, partial observability is utilized to obtain the object measurements and optimum box dimensions. This method is robust in tracking mobile objects under such situations as occlusion, new appearing and stablization, etc. Experimental results show that the proposed method is efficient.展开更多
Multiple Object Tracking(MOT)is essential for applications such as autonomous driving,surveillance,and analytics;However,challenges such as occlusion,low-resolution imaging,and identity switches remain persistent.We p...Multiple Object Tracking(MOT)is essential for applications such as autonomous driving,surveillance,and analytics;However,challenges such as occlusion,low-resolution imaging,and identity switches remain persistent.We propose HAMOT,a hierarchical adaptive multi-object tracker that solves these challenges with a novel,unified framework.Unlike previous methods that rely on isolated components,HAMOT incorporates a Swin Transformer-based Adaptive Enhancement(STAE)module—comprising Scene-Adaptive Transformer Enhancement and Confidence-Adaptive Feature Refinement—to improve detection under low-visibility conditions.The hierarchical DynamicGraphNeuralNetworkwith TemporalAttention(DGNN-TA)models both short-and long-termassociations,and the Adaptive Unscented Kalman Filter with Gated Recurrent Unit(AUKF-GRU)ensures accurate motion prediction.The novel Graph-Based Density-Aware Clustering(GDAC)improves occlusion recovery by adapting to scene density,preserving identity integrity.This integrated approach enables adaptive responses to complex visual scenarios,Achieving exceptional performance across all evaluation metrics,including aHigher Order TrackingAccuracy(HOTA)of 67.05%,a Multiple Object Tracking Accuracy(MOTA)of 82.4%,an ID F1 Score(IDF1)of 83.1%,and a total of 1052 Identity Switches(IDSW)on theMOT17;66.61%HOTA,78.3%MOTA,82.1%IDF1,and a total of 748 IDSWonMOT20;and 66.4%HOTA,92.32%MOTA,and 68.96%IDF1 on DanceTrack.With fixed thresholds,the full HAMOT model(all six components)achieves real-time functionality at 24 FPS on MOT17 using RTX3090,ensuring robustness and scalability for real-world MOT applications.展开更多
Multi-Object Tracking(MOT)represents a fundamental but computationally demanding task in computer vision,with particular challenges arising in occluded and densely populated environments.While contemporary tracking sy...Multi-Object Tracking(MOT)represents a fundamental but computationally demanding task in computer vision,with particular challenges arising in occluded and densely populated environments.While contemporary tracking systems have demonstrated considerable progress,persistent limitations—notably frequent occlusion-induced identity switches and tracking inaccuracies—continue to impede reliable real-world deployment.This work introduces an advanced tracking framework that enhances association robustness through a two-stage matching paradigm combining spatial and appearance features.Proposed framework employs:(1)a Height Modulated and Scale Adaptive Spatial Intersection-over-Union(HMSIoU)metric for improved spatial correspondence estimation across variable object scales and partial occlusions;(2)a feature extraction module generating discriminative appearance descriptors for identity maintenance;and(3)a recovery association mechanism for refining matches between unassociated tracks and detections.Comprehensive evaluation on standard MOT17 and MOT20 benchmarks demonstrates significant improvements in tracking consistency,with state-of-the-art performance across key metrics including HOTA(64),MOTA(80.7),IDF1(79.8),and IDs(1379).These results substantiate the efficacy of our Cue-Tracker framework in complex real-world scenarios characterized by occlusions and crowd interactions.展开更多
Multi-object tracking(MOT) techniques have been increasingly applied in a diverse range of tasks. Unmanned aerial vehicle(UAV) is one of its typical application scenarios. Due to the scene complexity and the low resol...Multi-object tracking(MOT) techniques have been increasingly applied in a diverse range of tasks. Unmanned aerial vehicle(UAV) is one of its typical application scenarios. Due to the scene complexity and the low resolution of moving targets in UAV applications, it is difficult to extract target features and identify them. In order to solve this problem, we propose a new re-identification(re-ID) network to extract association features for tracking in the association stage. Moreover, in order to reduce the complexity of detection model, we perform the lightweight optimization for it. Experimental results show that the proposed re-ID network can effectively reduce the number of identity switches, and surpass current state-of-the-art algorithms. In the meantime, the optimized detector can increase the speed by 27% owing to its lightweight design, which enables it to further meet the requirements of UAV tracking tasks.展开更多
Pig farmers want to have an effective solution for automatically detecting and tracking multiple pigs and alerting their conditions in order to recognize disease risk factors quickly.In this paper,therefore,we propose...Pig farmers want to have an effective solution for automatically detecting and tracking multiple pigs and alerting their conditions in order to recognize disease risk factors quickly.In this paper,therefore,we propose a novel monitoring system using an Artificial Intelligence of Things(AIoT)technique combining artificial intelligence and Internet of Things(IoT).The proposed system consists of AIoT edge devices and a central monitoring server.First,an AIoT edge device extracts video frame images from a CCTV camera installed in a pig pen by a frame extraction method,detects multiple pigs in the images by a faster region-based convolutional neural network(RCNN)model,and tracks them by an object center-point tracking algorithm(OCTA)based on bounding box regression outputs of the faster RCNN.Finally,it sends multi-pig tracking images to the central monitoring server,which alerts them to pig farmers through a social networking service(SNS)agent in cooperation with an oneM2M-compliant IoT alerting method.Experimental results showed that the multi-pig tracking method achieved the multi-object tracking accuracy performance of about 77%.In addition,we verified alerting operation by confirming the images received in the SNS smartphone application.展开更多
In order to solve the problem of small object size and low detection accuracy under the unmanned aerial vehicle(UAV)platform,the object detection algorithm based on deep aggregation network and high-resolution fusion ...In order to solve the problem of small object size and low detection accuracy under the unmanned aerial vehicle(UAV)platform,the object detection algorithm based on deep aggregation network and high-resolution fusion module is studied.Furthermore,a joint network of object detection and feature extraction is studied to construct a real-time multi-object tracking algorithm.For the problem of object association failure caused by UAV movement,image registration is applied to multi-object tracking and a camera motion discrimination model is proposed to improve the speed of the multi-object tracking algorithm.The simulation results show that the algorithm proposed in this study can improve the accuracy of multi-object tracking under the UAV platform,and effectively solve the problem of association failure caused by UAV movement.展开更多
Multi-object tracking(MOT)has seen rapid improvements in recent years.However,frequent occlusion remains a significant challenge in MOT,as it can cause targets to become smaller or disappear entirely,resulting in lowq...Multi-object tracking(MOT)has seen rapid improvements in recent years.However,frequent occlusion remains a significant challenge in MOT,as it can cause targets to become smaller or disappear entirely,resulting in lowquality targets,leading to trajectory interruptions and reduced tracking performance.Different from some existing methods,which discarded the low-quality targets or ignored low-quality target attributes.LQTTrack,with a lowquality association strategy(LQA),is proposed to pay more attention to low-quality targets.In the association scheme of LQTTrack,firstly,multi-scale feature fusion of FPN(MSFF-FPN)is utilized to enrich the feature information and assist in subsequent data association.Secondly,the normalized Wasserstein distance(NWD)is integrated to replace the original Inter over Union(IoU),thus overcoming the limitations of the traditional IoUbased methods that are sensitive to low-quality targets with small sizes and enhancing the robustness of low-quality target tracking.Moreover,the third association stage is proposed to improve the matching between the current frame’s low-quality targets and previously interrupted trajectories from earlier frames to reduce the problem of track fragmentation or error tracking,thereby increasing the association success rate and improving overall multi-object tracking performance.Extensive experimental results demonstrate the competitive performance of LQTTrack on benchmark datasets(MOT17,MOT20,and DanceTrack).展开更多
In this study,a multi-object tracking(MOT)scheme based on a light detection and ranging sensor was proposed to overcome imprecise velocity observations in object occlusion scenarios.By applying real-time velocity esti...In this study,a multi-object tracking(MOT)scheme based on a light detection and ranging sensor was proposed to overcome imprecise velocity observations in object occlusion scenarios.By applying real-time velocity estimation,a modified unscented Kalman filter(UKF)was proposed for the state estimation of a target object.The proposed method can reduce the calculation cost by obviating unscented transformations.Additionally,combined with the advantages of a two-reference-point selection scheme based on a center point and a corner point,a reference point switching approach was introduced to improve tracking accuracy and consistency.The state estimation capability of the proposed UKF was verified by comparing it with the standard UKF in single-target tracking simulations.Moreover,the performance of the proposed MOT system was evaluated using real traffic datasets.展开更多
An approach to track multiple objects in crowded scenes with long-term partial occlusions is proposed. Tracking-by-detection is a successful strategy to address the task of tracking multiple objects in unconstrained s...An approach to track multiple objects in crowded scenes with long-term partial occlusions is proposed. Tracking-by-detection is a successful strategy to address the task of tracking multiple objects in unconstrained scenarios,but an obvious shortcoming of this method is that most information available in image sequences is simply ignored due to thresholding weak detection responses and applying non-maximum suppression. This paper proposes a multi-label conditional random field（ CRF） model which integrates the superpixel information and detection responses into a unified energy optimization framework to handle the task of tracking multiple targets. A key characteristic of the model is that the pairwise potential is constructed to enforce collision avoidance between objects,which can offer the advantage to improve the tracking performance in crowded scenes. Experiments on standard benchmark databases demonstrate that the proposed algorithm significantly outperforms the state-of-the-art tracking-by-detection methods.展开更多
A simple yet efficient tracking framework is proposed for real-time multi-object tracking with micro aerial vehicles(MAVs). It's basic missions for MAVs to detect specific targets and then track them automatically...A simple yet efficient tracking framework is proposed for real-time multi-object tracking with micro aerial vehicles(MAVs). It's basic missions for MAVs to detect specific targets and then track them automatically. In our method, candidate regions are generated using the salient detection in each frame and then classified by an eural network. A kernelized correlation filter(KCF) is employed to track each target until it disappears or the peak-sidelobe ratio is lower than a threshold. Besides, we define the birth and death of each tracker for the targets. The tracker is recycled if its target disappears and can be assigned to a new target. The algorithm is evaluated on the PAFISS and UAV123 datasets. The results show a good performance on both the tracking accuracy and speed.展开更多
Multi-Object Tracking(MOT)is designed to accurately ascertain the positions and trajectories of moving objects within a video sequence.While prevalent methodologies primarily link detected objects across successive fr...Multi-Object Tracking(MOT)is designed to accurately ascertain the positions and trajectories of moving objects within a video sequence.While prevalent methodologies primarily link detected objects across successive frames by leveraging appearance and motion attributes,some approaches incorporate implicit global correlations from multiple antecedent frames to delineate target trajectories.Nonetheless,the capability to predict trajectories over multiple future frames remains insufficiently explored,leading to a significant underutilization of pertinent information in MOT.To address this gap,we introduce a transformer-based methodology,termed Preformer MOT,which enhances the precision of nonlinear trajectory predictions in dynamic settings.This enhancement is achieved through an innovative combination of a novel motion estimation technique-trajectory prediction-and Kalman filtering.Our method not only utilizes historical trajectory data but also anticipates the future positions of the target objects up to n subsequent steps,thereby furnishing a comprehensive prediction of trajectories with extensive temporal correlations.Specifically,we develop a straightforward self-supervised trajectory prediction model that estimates the future positions of a target object based on previously observed positional data.During the correlation phase,if a trajectory disruption occurs due to overlapping,occlusion,or nonlinear movements of the detected objects,Preformer MOT is capable of making early predictions using data from multiple forthcoming frames to reestablish trajectory continuity.Empirical evaluations on pedestrian datasets such as DanceTrack and MOT17 demonstrate that our approach surpasses other contemporary state-of-the-art methods.Furthermore,Preformer MOT exhibits exceptional performance in complex marine environments,underscoring its adaptability and efficacy.展开更多
Although the joint-detection-and-tracking paradigm has promoted the development of multi-object tracking(MOT)significantly,the long-term occlusion problem is still unsolved.After a period of trajectory inactivation du...Although the joint-detection-and-tracking paradigm has promoted the development of multi-object tracking(MOT)significantly,the long-term occlusion problem is still unsolved.After a period of trajectory inactivation due to occlusion,it is difficult to achieve trajectory reconnection with appearance features because they are no longer reliable.Although using motion cues does not suffer from occlusion,the commonly used Kalman Filter is also ineffective in its long-term inertia prediction in cases of no observation updates or wrong updates.Besides,occlusion is prone to cause multiple track-detection pairs to have close similarity scores during the data association phase.The direct use of the Hungarian algorithm to give the global optimal solution may generate the identity switching problem.In this paper,we propose the Long-term Spatio-Temporal Prediction(LSTP)module and the Ordered Association(OA)module to alleviate the occlusion problem in terms of motion prediction and data association,respectively.The LSTP module estimates the states of all tracked objects over time using a combination of spatial and temporal Transformers.The spatial Transformer models crowd interaction and learns the influence of neighbors,while the temporal Transformer models the temporal continuity of historical trajectories.Besides,the LSTP module also predicts the visibilities of the motion prediction boxes,which denote the occlusion attributes of trajectories.Based on the occlusion attribute and active state,the association priority is defined in the OA module to associate trajectories in order,which helps to alleviate the identity switching problem.Comprehensive experiments on the MOT17 and MOT20 benchmarks indicate the superiority of the proposed MOT framework,namely Occlusion-Robust Tracker(ORT).Without using any appearance information,our ORT can achieve competitive performance beyond other state-of-the-art trackers in terms of trajectory accuracy and purity.展开更多
As a vital technology in Cyber-Physical Social Intelligence (CPSI), Multi-Object-Tracking (MOT) can support comprehensive perception and analysis of the physical environment and social virtual space, promoting an in-d...As a vital technology in Cyber-Physical Social Intelligence (CPSI), Multi-Object-Tracking (MOT) can support comprehensive perception and analysis of the physical environment and social virtual space, promoting an in-depth understanding of human behavior, object movement, and social interaction. Most MOT methods often adopt simple interpolation or prediction strategies when dealing with temporarily lost targets, but ignore the comprehensive consideration of the state of the target before its reappearance. This approach may lead to an incomplete understanding of the target’s behavior and dynamics, which affects the accuracy and depth of the comprehensive understanding of social and physical space interactions in the real world. To improve it, we propose an online multi-object tracking method based on Record Confidence and Hierarchical Association (RCHA), which is represented as RCHA-Track. The Kalman filter combined with an Enhanced Correlation Coefficient (ECC) provides more accurate motion prediction under the influence of camera motion. The record confidence is designed to evaluate the loss status of the unseen object and refine the tracking trajectory. The normally tracked targets and the temporarily lost targets are combined to perform a hierarchical association based on the number of lost frames to achieve more accurate data associations. Compared with the latest ByteTrack, RCHA-Track improves MOTA, IDF1, and HOTA by 1.7%, 1.6%, and 1.3% on the benchmark dataset MOT17, and 1.3%, 2.1%, and 2.0% on MOT20, respectively, achieving state-of-the-art performance. Extensive ablation experiments demonstrate the effectiveness of each key module in the proposed RCHA-Track.展开更多
This article investigates the robust current tracking control problem of three-phase grid-connected inverters with LCL filter under external disturbance by a dynamic state feedback control method.First,this paper cons...This article investigates the robust current tracking control problem of three-phase grid-connected inverters with LCL filter under external disturbance by a dynamic state feedback control method.First,this paper constructs an internal model to learn the information of the states and input of the grid-connected inverter under steady state.Second,by utilizing the internal model principle,the paper turns the tracking control problem into the robust stabilization control problem based on some appropriate coordinate transformations.Then,The paper designs a dynamics state feedback control law to deal with this robust stabilization problem,and thus the solution of the robust current tracking control problem of three-phase grid-connected inverters can be obtained.This control method can ensure the asymptotic stability of the closedloop system.Finally,the paper illustrates the effectiveness of the proposed control approach through several groups of simulations,and compares it with the feedforward control method to verify the robustness of the proposed control method to uncertain parameters.展开更多
The focus of this paper is on distributed average tracking(DAT)in the context of external disturbances,utilizing an event-triggered control mechanism.First,an event-triggered anti-disturbance DAT(ETAD-DAT)algorithm is...The focus of this paper is on distributed average tracking(DAT)in the context of external disturbances,utilizing an event-triggered control mechanism.First,an event-triggered anti-disturbance DAT(ETAD-DAT)algorithm is proposed to reduce communication load in networked control systems by redesigning existing anti-disturbance DAT algorithms and disturbance observers.Furthermore,a fully distributed event-triggering condition is employed to schedule event times for each agent.Simulation results demonstrate that the proposed ETAD-DAT algorithm is able to achieve accurate average tracking of multiple time-varying reference signals despite the presence of external disturbances,while the communication efficiency can be improved obviously.展开更多
This paper investigates the challenges of structural inconsistency,matching accuracy degradation,and trajectory interruptions caused by high-speed motion,frequent occlusions,and appearance variations of unmanned aeria...This paper investigates the challenges of structural inconsistency,matching accuracy degradation,and trajectory interruptions caused by high-speed motion,frequent occlusions,and appearance variations of unmanned aerial vehicle(UAV) targets in low-altitude airspace.A novel UAV visual tracking method is proposed for dynamic structural distortions,with a focus on structural consistency modeling to improve system robustness in complex scenarios.Unlike prior methods such as STARK,which rely on spatio-temporal prediction,and KeepTrack,which emphasize template maintenance,our approach enforces structural-level consistency between historical and current features,thereby addressing UAV-specific issues of rapid maneuvering and environmental complexity.The proposed framework features a structure-aware architecture that incorporates dual complementary mechanisms serving as spatial completion and temporal restoration components.First,a multi-scale structure extraction module with adaptive anchor scheduling is developed to dynamically perceive spatial target shape and generate high-quality proposals.Second,a structural memory module is designed to reconstruct local regions by leveraging high-confidence historical structural representations,thereby maintaining spatiotemporal coherence across frames.Furthermore,a structural verification mechanism coupled with a meta-learning-driven re-identification strategy is introduced to detect abrupt structural distortions and adaptively update templates,significantly improving resilience against disturbances.Overall,the main contributions of this paper can be summarized as follows:(1) introducing structural consistency modeling into UAV visual tracking for the first time;(2) designing a unified framework that combines adaptive proposal generation,full-image matching,and re-identification under structural constraints;and(3) achieving state-of-the-art performance on the anti-UAV benchmark,highlighting the method's practical value in real-world UAV surveillance applications.展开更多
This article proposes a Gaussian process(GP) based model predictive control(MPC) method to solve the tracking control of wheeled mobile robot( WMR) with uncertain model parameters.Firstly,a Gaussian process velocity p...This article proposes a Gaussian process(GP) based model predictive control(MPC) method to solve the tracking control of wheeled mobile robot( WMR) with uncertain model parameters.Firstly,a Gaussian process velocity prediction model is proposed to compensate for the unknown dynamic model,as the kinematic model cannot accurately characterize the motion characteristics of the robot.Then,by introducing the Lorentz function,the improved iterative linear quadratic regulator(iLQR) method is used to solve the nonlinear MPC(NMPC) controller with constraints.In addition,in order to reduce computational burden,a closed gradient calculation method is introduced to improve algorithm efficiency.Finally,the feasibility and effectiveness of this method are verified through simulation and experiment.展开更多
Great Snipe(Gallinago media) is a shore bird which has a Near Threatened status on the global scale.However,little is known about its migration strategy from the breeding range in Russia.This study is the first one ai...Great Snipe(Gallinago media) is a shore bird which has a Near Threatened status on the global scale.However,little is known about its migration strategy from the breeding range in Russia.This study is the first one aiming to reveal migration routes,stopovers and wintering grounds of adult Great Snipes from their breeding range in Russia using GPS devices.We also analyzed connectivity of Great Snipes from different breeding populations of this species during non-breeding season.In 2021,we equipped seven males and three females with satellite transmitters,ICARUS Basic Tags,in the breeding range in central European Russia(56°75′N,37°65 E).One female appeared later in tundra of north-eastern Europe.In the second half of July to early September,birds migrated to Africa in a fairly wide front and made stopovers in Europe before crossing seas and the Sahara.Our data allowed to suppose high mortality of birds on migration,especially during the trans-Saharan flight.Only four Great Snipes reached Africa alive during southward migration.These birds spread over across wide area from Eritrea to Ghana after the trans-Saharan flight,after which they moved in a general westward direction and made final prolonged stopovers in Ghana or to the south of Chad Lake.In October/December birds relocated to wintering grounds in Sub-Equatorial Afrotropics as far as the south of Democratic Republic of the Congo and Zambia;with intermediate winter sites in low and middle reaches of the Congo Basin.Together with other published results,our data showed wide overlap of African non-breeding grounds of birds coming from lowland Eastern European and mountain Scandinavian breeding populations.The results also indicated insufficient conservation status of migration stopovers and wintering sites,used by Great Snipes,and demonstrated high importance of West Africa for conservation of this species.展开更多
基金funded by the Fundamental Research Funds for the Central Universities(Grant No.106-YDZX2025022)the Startup Foundation of New Professor at Nanjing Agricultural University(Grant No.106-804005)the“Qing Lan Project”of Jiangsu Higher Education Institutions.
文摘Understanding fish movement trajectories in aquaculture is essential for practical applications,such as disease warning,feeding optimization,and breeding management.These trajectories reveal key information about the fish’s behavior,health,and environmental adaptability.However,when multi-object tracking(MOT)algorithms are applied to the high-density aquaculture environment,occlusion and overlapping among fish may result in missed detections,false detections,and identity switching problems,which limit the tracking accuracy.To address these issues,this paper proposes FishTracker,a MOT algorithm,by utilizing a Tracking-by-Detection framework.First,the neck part of the YOLOv8 model is enhanced by introducing a Multi-Scale Dilated Attention(MSDA)module to improve object localization and classification confidence.Second,an Adaptive Kalman Filter(AKF)is employed in the tracking phase to dynamically adjust motion prediction parameters,thereby overcoming target adhesion and nonlinear motion in complex scenarios.Experimental results show that FishTracker achieves a multi-object tracking accuracy(MOTA)of 93.22% and 87.24% in bright and dark illumination conditions,respectively.Further validation in a real aquaculture scenario reveal that FishTracker achieves aMOTA of 76.70%,which is 5.34% higher than the baselinemodel.The higher order tracking accuracy(HOTA)reaches 50.5%,which is 3.4% higher than the benchmark.In conclusion,FishTracker can provide reliable technical support for accurate tracking and behavioral analysis of high-density fish populations.
基金supported by the confidential research grant No.a8317。
文摘To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.
基金The National Natural Science Foundation of China(No.60574006,60804017)
文摘To cope with multi-object tracking under real-world complex situations, a new video-based method is proposed. In the detecting step, the moving objects are segmented with the third level DWT （discrete wavelet transform ）and background difference. In the tracking step, the Kalman filter and scale parameter are used first to estimate the object position and bounding box. Then, the center-association-based projection ratio and region-association-based occlusion ratio are defined and combined to judge object behaviours. Finally, the tracking scheme and Kalman parameters are adaptively adjusted according to object behaviour. Under occlusion, partial observability is utilized to obtain the object measurements and optimum box dimensions. This method is robust in tracking mobile objects under such situations as occlusion, new appearing and stablization, etc. Experimental results show that the proposed method is efficient.
基金supported in part by Multimedia University under the Research Fellow Grant MMUI/250008in part by Telekom Research&Development Sdn Bhd under Grants RDTC/241149 and RDTC/231095+1 种基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R140)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Multiple Object Tracking(MOT)is essential for applications such as autonomous driving,surveillance,and analytics;However,challenges such as occlusion,low-resolution imaging,and identity switches remain persistent.We propose HAMOT,a hierarchical adaptive multi-object tracker that solves these challenges with a novel,unified framework.Unlike previous methods that rely on isolated components,HAMOT incorporates a Swin Transformer-based Adaptive Enhancement(STAE)module—comprising Scene-Adaptive Transformer Enhancement and Confidence-Adaptive Feature Refinement—to improve detection under low-visibility conditions.The hierarchical DynamicGraphNeuralNetworkwith TemporalAttention(DGNN-TA)models both short-and long-termassociations,and the Adaptive Unscented Kalman Filter with Gated Recurrent Unit(AUKF-GRU)ensures accurate motion prediction.The novel Graph-Based Density-Aware Clustering(GDAC)improves occlusion recovery by adapting to scene density,preserving identity integrity.This integrated approach enables adaptive responses to complex visual scenarios,Achieving exceptional performance across all evaluation metrics,including aHigher Order TrackingAccuracy(HOTA)of 67.05%,a Multiple Object Tracking Accuracy(MOTA)of 82.4%,an ID F1 Score(IDF1)of 83.1%,and a total of 1052 Identity Switches(IDSW)on theMOT17;66.61%HOTA,78.3%MOTA,82.1%IDF1,and a total of 748 IDSWonMOT20;and 66.4%HOTA,92.32%MOTA,and 68.96%IDF1 on DanceTrack.With fixed thresholds,the full HAMOT model(all six components)achieves real-time functionality at 24 FPS on MOT17 using RTX3090,ensuring robustness and scalability for real-world MOT applications.
文摘Multi-Object Tracking(MOT)represents a fundamental but computationally demanding task in computer vision,with particular challenges arising in occluded and densely populated environments.While contemporary tracking systems have demonstrated considerable progress,persistent limitations—notably frequent occlusion-induced identity switches and tracking inaccuracies—continue to impede reliable real-world deployment.This work introduces an advanced tracking framework that enhances association robustness through a two-stage matching paradigm combining spatial and appearance features.Proposed framework employs:(1)a Height Modulated and Scale Adaptive Spatial Intersection-over-Union(HMSIoU)metric for improved spatial correspondence estimation across variable object scales and partial occlusions;(2)a feature extraction module generating discriminative appearance descriptors for identity maintenance;and(3)a recovery association mechanism for refining matches between unassociated tracks and detections.Comprehensive evaluation on standard MOT17 and MOT20 benchmarks demonstrates significant improvements in tracking consistency,with state-of-the-art performance across key metrics including HOTA(64),MOTA(80.7),IDF1(79.8),and IDs(1379).These results substantiate the efficacy of our Cue-Tracker framework in complex real-world scenarios characterized by occlusions and crowd interactions.
基金supported by the Research Foundation of Nanjing University of Posts and Telecommunications (No.NY219076)。
文摘Multi-object tracking(MOT) techniques have been increasingly applied in a diverse range of tasks. Unmanned aerial vehicle(UAV) is one of its typical application scenarios. Due to the scene complexity and the low resolution of moving targets in UAV applications, it is difficult to extract target features and identify them. In order to solve this problem, we propose a new re-identification(re-ID) network to extract association features for tracking in the association stage. Moreover, in order to reduce the complexity of detection model, we perform the lightweight optimization for it. Experimental results show that the proposed re-ID network can effectively reduce the number of identity switches, and surpass current state-of-the-art algorithms. In the meantime, the optimized detector can increase the speed by 27% owing to its lightweight design, which enables it to further meet the requirements of UAV tracking tasks.
基金supported by Institute of Information&communications Technology Planning&Evaluation(IITP)Grant funded by the Korea government(MSIT)(No.2018-0-00387Development of ICT based Intelligent Smart Welfare Housing System for the Prevention and Control of Livestock Disease).
文摘Pig farmers want to have an effective solution for automatically detecting and tracking multiple pigs and alerting their conditions in order to recognize disease risk factors quickly.In this paper,therefore,we propose a novel monitoring system using an Artificial Intelligence of Things(AIoT)technique combining artificial intelligence and Internet of Things(IoT).The proposed system consists of AIoT edge devices and a central monitoring server.First,an AIoT edge device extracts video frame images from a CCTV camera installed in a pig pen by a frame extraction method,detects multiple pigs in the images by a faster region-based convolutional neural network(RCNN)model,and tracks them by an object center-point tracking algorithm(OCTA)based on bounding box regression outputs of the faster RCNN.Finally,it sends multi-pig tracking images to the central monitoring server,which alerts them to pig farmers through a social networking service(SNS)agent in cooperation with an oneM2M-compliant IoT alerting method.Experimental results showed that the multi-pig tracking method achieved the multi-object tracking accuracy performance of about 77%.In addition,we verified alerting operation by confirming the images received in the SNS smartphone application.
基金the National Natural Science Foundation of China (No.61627810)the National Science and Technology Major Program of China (No.2018YFB1305003)the National Defense Science and Technology Outstanding Youth Science Foundation (No.2017-JCJQ-ZQ-031)。
文摘In order to solve the problem of small object size and low detection accuracy under the unmanned aerial vehicle(UAV)platform,the object detection algorithm based on deep aggregation network and high-resolution fusion module is studied.Furthermore,a joint network of object detection and feature extraction is studied to construct a real-time multi-object tracking algorithm.For the problem of object association failure caused by UAV movement,image registration is applied to multi-object tracking and a camera motion discrimination model is proposed to improve the speed of the multi-object tracking algorithm.The simulation results show that the algorithm proposed in this study can improve the accuracy of multi-object tracking under the UAV platform,and effectively solve the problem of association failure caused by UAV movement.
基金supported by the National Natural Science Foundation of China(No.62202143)Key Research and Promotion Projects of Henan Province(Nos.232102240023,232102210063,222102210040).
文摘Multi-object tracking(MOT)has seen rapid improvements in recent years.However,frequent occlusion remains a significant challenge in MOT,as it can cause targets to become smaller or disappear entirely,resulting in lowquality targets,leading to trajectory interruptions and reduced tracking performance.Different from some existing methods,which discarded the low-quality targets or ignored low-quality target attributes.LQTTrack,with a lowquality association strategy(LQA),is proposed to pay more attention to low-quality targets.In the association scheme of LQTTrack,firstly,multi-scale feature fusion of FPN(MSFF-FPN)is utilized to enrich the feature information and assist in subsequent data association.Secondly,the normalized Wasserstein distance(NWD)is integrated to replace the original Inter over Union(IoU),thus overcoming the limitations of the traditional IoUbased methods that are sensitive to low-quality targets with small sizes and enhancing the robustness of low-quality target tracking.Moreover,the third association stage is proposed to improve the matching between the current frame’s low-quality targets and previously interrupted trajectories from earlier frames to reduce the problem of track fragmentation or error tracking,thereby increasing the association success rate and improving overall multi-object tracking performance.Extensive experimental results demonstrate the competitive performance of LQTTrack on benchmark datasets(MOT17,MOT20,and DanceTrack).
基金the National Natural Science Foundation of China(No.51775331)。
文摘In this study,a multi-object tracking(MOT)scheme based on a light detection and ranging sensor was proposed to overcome imprecise velocity observations in object occlusion scenarios.By applying real-time velocity estimation,a modified unscented Kalman filter(UKF)was proposed for the state estimation of a target object.The proposed method can reduce the calculation cost by obviating unscented transformations.Additionally,combined with the advantages of a two-reference-point selection scheme based on a center point and a corner point,a reference point switching approach was introduced to improve tracking accuracy and consistency.The state estimation capability of the proposed UKF was verified by comparing it with the standard UKF in single-target tracking simulations.Moreover,the performance of the proposed MOT system was evaluated using real traffic datasets.
基金Supported by the National Natural Science Foundation of China(61471225)Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents(2014RCJJ055)
文摘An approach to track multiple objects in crowded scenes with long-term partial occlusions is proposed. Tracking-by-detection is a successful strategy to address the task of tracking multiple objects in unconstrained scenarios,but an obvious shortcoming of this method is that most information available in image sequences is simply ignored due to thresholding weak detection responses and applying non-maximum suppression. This paper proposes a multi-label conditional random field（ CRF） model which integrates the superpixel information and detection responses into a unified energy optimization framework to handle the task of tracking multiple targets. A key characteristic of the model is that the pairwise potential is constructed to enforce collision avoidance between objects,which can offer the advantage to improve the tracking performance in crowded scenes. Experiments on standard benchmark databases demonstrate that the proposed algorithm significantly outperforms the state-of-the-art tracking-by-detection methods.
基金Supported by the National Natural Science Foundation of China(6160303040,61433003)Yunnan Applied Basic Research Project of China(201701CF00037)Yunnan Provincial Science and Technology Department Key Research Program(Engineering)(2018BA070)
文摘A simple yet efficient tracking framework is proposed for real-time multi-object tracking with micro aerial vehicles(MAVs). It's basic missions for MAVs to detect specific targets and then track them automatically. In our method, candidate regions are generated using the salient detection in each frame and then classified by an eural network. A kernelized correlation filter(KCF) is employed to track each target until it disappears or the peak-sidelobe ratio is lower than a threshold. Besides, we define the birth and death of each tracker for the targets. The tracker is recycled if its target disappears and can be assigned to a new target. The algorithm is evaluated on the PAFISS and UAV123 datasets. The results show a good performance on both the tracking accuracy and speed.
基金supported by the National Natural Science Foun-dation of China under 6237326662421004+3 种基金62122046U24A2027962473243supported by the Shanghai Commission of Science and Technology,23010500100.
文摘Multi-Object Tracking(MOT)is designed to accurately ascertain the positions and trajectories of moving objects within a video sequence.While prevalent methodologies primarily link detected objects across successive frames by leveraging appearance and motion attributes,some approaches incorporate implicit global correlations from multiple antecedent frames to delineate target trajectories.Nonetheless,the capability to predict trajectories over multiple future frames remains insufficiently explored,leading to a significant underutilization of pertinent information in MOT.To address this gap,we introduce a transformer-based methodology,termed Preformer MOT,which enhances the precision of nonlinear trajectory predictions in dynamic settings.This enhancement is achieved through an innovative combination of a novel motion estimation technique-trajectory prediction-and Kalman filtering.Our method not only utilizes historical trajectory data but also anticipates the future positions of the target objects up to n subsequent steps,thereby furnishing a comprehensive prediction of trajectories with extensive temporal correlations.Specifically,we develop a straightforward self-supervised trajectory prediction model that estimates the future positions of a target object based on previously observed positional data.During the correlation phase,if a trajectory disruption occurs due to overlapping,occlusion,or nonlinear movements of the detected objects,Preformer MOT is capable of making early predictions using data from multiple forthcoming frames to reestablish trajectory continuity.Empirical evaluations on pedestrian datasets such as DanceTrack and MOT17 demonstrate that our approach surpasses other contemporary state-of-the-art methods.Furthermore,Preformer MOT exhibits exceptional performance in complex marine environments,underscoring its adaptability and efficacy.
基金supported by the National Key Laboratory Foundation of Science and Technology on Multispectral Information Processing(6142113220208)Science and Technology Plan Project of Jinan(202214002)National Natural Science Foundation of China(61105006).
文摘Although the joint-detection-and-tracking paradigm has promoted the development of multi-object tracking(MOT)significantly,the long-term occlusion problem is still unsolved.After a period of trajectory inactivation due to occlusion,it is difficult to achieve trajectory reconnection with appearance features because they are no longer reliable.Although using motion cues does not suffer from occlusion,the commonly used Kalman Filter is also ineffective in its long-term inertia prediction in cases of no observation updates or wrong updates.Besides,occlusion is prone to cause multiple track-detection pairs to have close similarity scores during the data association phase.The direct use of the Hungarian algorithm to give the global optimal solution may generate the identity switching problem.In this paper,we propose the Long-term Spatio-Temporal Prediction(LSTP)module and the Ordered Association(OA)module to alleviate the occlusion problem in terms of motion prediction and data association,respectively.The LSTP module estimates the states of all tracked objects over time using a combination of spatial and temporal Transformers.The spatial Transformer models crowd interaction and learns the influence of neighbors,while the temporal Transformer models the temporal continuity of historical trajectories.Besides,the LSTP module also predicts the visibilities of the motion prediction boxes,which denote the occlusion attributes of trajectories.Based on the occlusion attribute and active state,the association priority is defined in the OA module to associate trajectories in order,which helps to alleviate the identity switching problem.Comprehensive experiments on the MOT17 and MOT20 benchmarks indicate the superiority of the proposed MOT framework,namely Occlusion-Robust Tracker(ORT).Without using any appearance information,our ORT can achieve competitive performance beyond other state-of-the-art trackers in terms of trajectory accuracy and purity.
基金supported by the National Natural Science Foundation of China(No.62302130).
文摘As a vital technology in Cyber-Physical Social Intelligence (CPSI), Multi-Object-Tracking (MOT) can support comprehensive perception and analysis of the physical environment and social virtual space, promoting an in-depth understanding of human behavior, object movement, and social interaction. Most MOT methods often adopt simple interpolation or prediction strategies when dealing with temporarily lost targets, but ignore the comprehensive consideration of the state of the target before its reappearance. This approach may lead to an incomplete understanding of the target’s behavior and dynamics, which affects the accuracy and depth of the comprehensive understanding of social and physical space interactions in the real world. To improve it, we propose an online multi-object tracking method based on Record Confidence and Hierarchical Association (RCHA), which is represented as RCHA-Track. The Kalman filter combined with an Enhanced Correlation Coefficient (ECC) provides more accurate motion prediction under the influence of camera motion. The record confidence is designed to evaluate the loss status of the unseen object and refine the tracking trajectory. The normally tracked targets and the temporarily lost targets are combined to perform a hierarchical association based on the number of lost frames to achieve more accurate data associations. Compared with the latest ByteTrack, RCHA-Track improves MOTA, IDF1, and HOTA by 1.7%, 1.6%, and 1.3% on the benchmark dataset MOT17, and 1.3%, 2.1%, and 2.0% on MOT20, respectively, achieving state-of-the-art performance. Extensive ablation experiments demonstrate the effectiveness of each key module in the proposed RCHA-Track.
基金Supported by the Fundamental Research Funds for the Central Universities(2024ZYGXZR047)the National Natural Science Foundation of China(62373156)the Guangdong Basic and Applied Basic Research Foundation(2024A1515011736)。
文摘This article investigates the robust current tracking control problem of three-phase grid-connected inverters with LCL filter under external disturbance by a dynamic state feedback control method.First,this paper constructs an internal model to learn the information of the states and input of the grid-connected inverter under steady state.Second,by utilizing the internal model principle,the paper turns the tracking control problem into the robust stabilization control problem based on some appropriate coordinate transformations.Then,The paper designs a dynamics state feedback control law to deal with this robust stabilization problem,and thus the solution of the robust current tracking control problem of three-phase grid-connected inverters can be obtained.This control method can ensure the asymptotic stability of the closedloop system.Finally,the paper illustrates the effectiveness of the proposed control approach through several groups of simulations,and compares it with the feedforward control method to verify the robustness of the proposed control method to uncertain parameters.
基金part supported by the National Natural Science Foundation(62203034,62273126,62203035)the Ling-Yan Research and Development Project of Zhejiang Province of China(2023C03185)。
文摘The focus of this paper is on distributed average tracking(DAT)in the context of external disturbances,utilizing an event-triggered control mechanism.First,an event-triggered anti-disturbance DAT(ETAD-DAT)algorithm is proposed to reduce communication load in networked control systems by redesigning existing anti-disturbance DAT algorithms and disturbance observers.Furthermore,a fully distributed event-triggering condition is employed to schedule event times for each agent.Simulation results demonstrate that the proposed ETAD-DAT algorithm is able to achieve accurate average tracking of multiple time-varying reference signals despite the presence of external disturbances,while the communication efficiency can be improved obviously.
基金Supported by the National Science Foundation of China (No.62571164)the Natural Science Foundation of Heilongjiang Province (No.PL2024F025)the Fundamental Scientific Research Funds of Heilongjiang Province (No.2022-KYYWF-1050)。
文摘This paper investigates the challenges of structural inconsistency,matching accuracy degradation,and trajectory interruptions caused by high-speed motion,frequent occlusions,and appearance variations of unmanned aerial vehicle(UAV) targets in low-altitude airspace.A novel UAV visual tracking method is proposed for dynamic structural distortions,with a focus on structural consistency modeling to improve system robustness in complex scenarios.Unlike prior methods such as STARK,which rely on spatio-temporal prediction,and KeepTrack,which emphasize template maintenance,our approach enforces structural-level consistency between historical and current features,thereby addressing UAV-specific issues of rapid maneuvering and environmental complexity.The proposed framework features a structure-aware architecture that incorporates dual complementary mechanisms serving as spatial completion and temporal restoration components.First,a multi-scale structure extraction module with adaptive anchor scheduling is developed to dynamically perceive spatial target shape and generate high-quality proposals.Second,a structural memory module is designed to reconstruct local regions by leveraging high-confidence historical structural representations,thereby maintaining spatiotemporal coherence across frames.Furthermore,a structural verification mechanism coupled with a meta-learning-driven re-identification strategy is introduced to detect abrupt structural distortions and adaptively update templates,significantly improving resilience against disturbances.Overall,the main contributions of this paper can be summarized as follows:(1) introducing structural consistency modeling into UAV visual tracking for the first time;(2) designing a unified framework that combines adaptive proposal generation,full-image matching,and re-identification under structural constraints;and(3) achieving state-of-the-art performance on the anti-UAV benchmark,highlighting the method's practical value in real-world UAV surveillance applications.
基金Supported by the Zhejiang Provincial Natural Science Foundation of China (No.LR23F030002)。
文摘This article proposes a Gaussian process(GP) based model predictive control(MPC) method to solve the tracking control of wheeled mobile robot( WMR) with uncertain model parameters.Firstly,a Gaussian process velocity prediction model is proposed to compensate for the unknown dynamic model,as the kinematic model cannot accurately characterize the motion characteristics of the robot.Then,by introducing the Lorentz function,the improved iterative linear quadratic regulator(iLQR) method is used to solve the nonlinear MPC(NMPC) controller with constraints.In addition,in order to reduce computational burden,a closed gradient calculation method is introduced to improve algorithm efficiency.Finally,the feasibility and effectiveness of this method are verified through simulation and experiment.
基金A support for different activities in the framework of this research was provided by Max Planck Institute of Animal Behavior,the Russian space agency (Roskosmos),German Aerospace Center and Institute for Geography of the Russian Academy of Sciences via International Cooperation for Animal Research Using Space as well as by NABU–The Nature and Biodiversity Conservation Union2021 was carried out as a joint project of Birds Russia and Manfred-Hermsen Stiftung+2 种基金funded under state assignments of the Severtsov Institute of Ecology and Evolution of the Russian Academy of Science (No. 0089-2021-0004, FFER-2024-0013No. 0089-2021-0010, FFER-2024-0022No. 1022040700480-0-1.6.15)。
文摘Great Snipe(Gallinago media) is a shore bird which has a Near Threatened status on the global scale.However,little is known about its migration strategy from the breeding range in Russia.This study is the first one aiming to reveal migration routes,stopovers and wintering grounds of adult Great Snipes from their breeding range in Russia using GPS devices.We also analyzed connectivity of Great Snipes from different breeding populations of this species during non-breeding season.In 2021,we equipped seven males and three females with satellite transmitters,ICARUS Basic Tags,in the breeding range in central European Russia(56°75′N,37°65 E).One female appeared later in tundra of north-eastern Europe.In the second half of July to early September,birds migrated to Africa in a fairly wide front and made stopovers in Europe before crossing seas and the Sahara.Our data allowed to suppose high mortality of birds on migration,especially during the trans-Saharan flight.Only four Great Snipes reached Africa alive during southward migration.These birds spread over across wide area from Eritrea to Ghana after the trans-Saharan flight,after which they moved in a general westward direction and made final prolonged stopovers in Ghana or to the south of Chad Lake.In October/December birds relocated to wintering grounds in Sub-Equatorial Afrotropics as far as the south of Democratic Republic of the Congo and Zambia;with intermediate winter sites in low and middle reaches of the Congo Basin.Together with other published results,our data showed wide overlap of African non-breeding grounds of birds coming from lowland Eastern European and mountain Scandinavian breeding populations.The results also indicated insufficient conservation status of migration stopovers and wintering sites,used by Great Snipes,and demonstrated high importance of West Africa for conservation of this species.
