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
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
Purpose-With the rapid advancement of China’s high-speed rail network,the density of train operations is on the rise.To address the challenge of shortening train tracking intervals while enhancing transportation effi...Purpose-With the rapid advancement of China’s high-speed rail network,the density of train operations is on the rise.To address the challenge of shortening train tracking intervals while enhancing transportation efficiency,the multi-objective dynamic optimization of the train operation process has emerged as a critical issue.Design/methodology/approach-Train dynamic model is established by analyzing the force of the train in the process of tracing operation.The train tracing operation model is established according to the dynamic mechanical model of the train tracking process,and the dynamic optimization analysis is carried out with comfort,energy saving and punctuality as optimization objectives.To achieve multi-objective dynamic optimization,a novel train tracking operation calculation method is proposed,utilizing the improved grey wolf optimization algorithm(MOGWO).The proposed method is simulated and verified based on the train characteristics and line data of CR400AF electric multiple units.Findings-The simulation results prove that the optimized MOGWO algorithm can be computed quickly during train tracks,the optimum results can be given within 5s and the algorithm can converge effectively in different optimization target directions.The optimized speed profile of the MOGWO algorithm is smoother and more stable and meets the target requirements of energy saving,punctuality and comfort while maximally respecting the speed limit profile.Originality/value-The MOGWO train tracking interval optimization method enhances the tracking process while ensuring a safe tracking interval.This approach enables the trailing train to operate more comfortably,energy-efficiently and punctually,aligning with passenger needs and industry trends.The method offers valuable insights for optimizing the high-speed train tracking process.展开更多
In the parallel steering coordination control strategy for path tracking,it is difficult to match the current driver steering model using the fixed parameters with the actual driver,and the designed steering coordinat...In the parallel steering coordination control strategy for path tracking,it is difficult to match the current driver steering model using the fixed parameters with the actual driver,and the designed steering coordination control strategy under a single objective and simple conditions is difficult to adapt to the multi-dimensional state variables’input.In this paper,we propose a deep reinforcement learning algorithm-based multi-objective parallel human-machine steering coordination strategy for path tracking considering driver misoperation and external disturbance.Firstly,the driver steering mathematical model is constructed based on the driver preview characteristics and steering delay response,and the driver characteristic parameters are fitted after collecting the actual driver driving data.Secondly,considering that the vehicle is susceptible to the influence of external disturbances during the driving process,the Tube MPC(Tube Model Predictive Control)based path tracking steering controller is designed based on the vehicle system dynamics error model.After verifying that the driver steering model meets the driver steering operation characteristics,DQN(Deep Q-network),DDPG(Deep Deterministic Policy Gradient)and TD3(Twin Delayed Deep Deterministic Policy Gradient)deep reinforcement learning algorithms are utilized to design a multi-objective parallel steering coordination strategy which satisfies the multi-dimensional state variables’input of the vehicle.Finally,the tracking accuracy,lateral safety,human-machine conflict and driver steering load evaluation index are designed in different driver operation states and different road environments,and the performance of the parallel steering coordination control strategies with different deep reinforcement learning algorithms and fuzzy algorithms are compared by simulations and hardware in the loop experiments.The results show that the parallel steering collaborative strategy based on a deep reinforcement learning algorithm can more effectively assist the driver in tracking the target path under lateral wind interference and driver misoperation,and the TD3-based coordination control strategy has better overall performance.展开更多
Background With the rapid development of Web3D, virtual reality, and digital twins, virtual trajectories and decision data considerably rely on the analysis and understanding of real video data, particularly in emerge...Background With the rapid development of Web3D, virtual reality, and digital twins, virtual trajectories and decision data considerably rely on the analysis and understanding of real video data, particularly in emergency evacuation scenarios. Correctly and effectively evacuating crowds in virtual emergency scenarios are becoming increasingly urgent. One good solution is to extract pedestrian trajectories from videos of emergency situations using a multi-target tracking algorithm and use them to define evacuation procedures. Methods To implement this solution, a trajectory extraction and optimization framework based on multi-target tracking is developed in this study. First, a multi-target tracking algorithm is used to extract and preprocess the trajectory data of the crowd in a video. Then, the trajectory is optimized by combining the trajectory point extraction algorithm and Savitzky-Golay smoothing filtering method. Finally, related experiments are conducted, and the results show that the proposed approach can effectively and accurately extract the trajectories of multiple target objects in real time. Results In addition, the proposed approach retains the real characteristics of the trajectories as much as possible while improving the trajectory smoothing index, which can provide data support for the analysis of pedestrian trajectory data and formulation of personnel evacuation schemes in emergency scenarios. Conclusions Further comparisons with methods used in related studies confirm the feasibility and superiority of the proposed framework.展开更多
Target tracking in wireless sensor network usually schedules a subset of sensor nodes to constitute a tasking cluster to collaboratively track a target.For the goals of saving energy consumption,prolonging network lif...Target tracking in wireless sensor network usually schedules a subset of sensor nodes to constitute a tasking cluster to collaboratively track a target.For the goals of saving energy consumption,prolonging network lifetime and improving tracking accuracy,sensor node scheduling for target tracking is indeed a multi-objective optimization problem.In this paper,a multi-objective optimization sensor node scheduling algorithm is proposed.It employs the unscented Kalman filtering algorithm for target state estimation and establishes tracking accuracy index,predicts the energy consumption of candidate sensor nodes,analyzes the relationship between network lifetime and remaining energy balance so as to construct energy efficiency index.Simulation results show that,compared with the existing sensor node scheduling,our proposed algorithm can achieve superior tracking accuracy and energy efficiency.展开更多
This paper studies the tracking control problem for stratospheric airships with userspecified performance.Dealing with the infinite gain phenomenon in the prescribed-time stability,a new stability criterion with bound...This paper studies the tracking control problem for stratospheric airships with userspecified performance.Dealing with the infinite gain phenomenon in the prescribed-time stability,a new stability criterion with bounded gain is proposed by using a new time-varying scaling function.Moreover,a same-side performance function and a novel barrier Lyapunov function are incorporated into the control algorithm,which can compress the feasible domain of tracking error to minimize the overshoot and solve the difficult in tracking error not converging to zero simultaneously.The proposed scheme guarantees the airship capable of operating autonomously with satisfactory transient performance and tracking accuracy,where the performance parameters can be designed artificially and link to the physical process directly.Finally,the effectiveness of the proposed control scheme is verified by theoretical analysis and numerical simulation.展开更多
This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hype...This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles.In order to increase the control amount,this online control legislation makes use of model predictive control(MPC)that is based on the concept of iterative learning control(ILC).By using offline data to decrease the linearized model’s faults,the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed.An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree.During the derivation process,a linearized model of longitudinal dynamics is established.The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.展开更多
Environment perception is one of the most critical technology of intelligent transportation systems(ITS).Motion interaction between multiple vehicles in ITS makes it important to perform multi-object tracking(MOT).How...Environment perception is one of the most critical technology of intelligent transportation systems(ITS).Motion interaction between multiple vehicles in ITS makes it important to perform multi-object tracking(MOT).However,most existing MOT algorithms follow the tracking-by-detection framework,which separates detection and tracking into two independent segments and limit the global efciency.Recently,a few algorithms have combined feature extraction into one network;however,the tracking portion continues to rely on data association,and requires com‑plex post-processing for life cycle management.Those methods do not combine detection and tracking efciently.This paper presents a novel network to realize joint multi-object detection and tracking in an end-to-end manner for ITS,named as global correlation network(GCNet).Unlike most object detection methods,GCNet introduces a global correlation layer for regression of absolute size and coordinates of bounding boxes,instead of ofsetting predictions.The pipeline of detection and tracking in GCNet is conceptually simple,and does not require compli‑cated tracking strategies such as non-maximum suppression and data association.GCNet was evaluated on a multivehicle tracking dataset,UA-DETRAC,demonstrating promising performance compared to state-of-the-art detectors and trackers.展开更多
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 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funded by the China Academy of Railway Sciences Corporation Limited Scientific Research Project(No:2023YJ080).
文摘Purpose-With the rapid advancement of China’s high-speed rail network,the density of train operations is on the rise.To address the challenge of shortening train tracking intervals while enhancing transportation efficiency,the multi-objective dynamic optimization of the train operation process has emerged as a critical issue.Design/methodology/approach-Train dynamic model is established by analyzing the force of the train in the process of tracing operation.The train tracing operation model is established according to the dynamic mechanical model of the train tracking process,and the dynamic optimization analysis is carried out with comfort,energy saving and punctuality as optimization objectives.To achieve multi-objective dynamic optimization,a novel train tracking operation calculation method is proposed,utilizing the improved grey wolf optimization algorithm(MOGWO).The proposed method is simulated and verified based on the train characteristics and line data of CR400AF electric multiple units.Findings-The simulation results prove that the optimized MOGWO algorithm can be computed quickly during train tracks,the optimum results can be given within 5s and the algorithm can converge effectively in different optimization target directions.The optimized speed profile of the MOGWO algorithm is smoother and more stable and meets the target requirements of energy saving,punctuality and comfort while maximally respecting the speed limit profile.Originality/value-The MOGWO train tracking interval optimization method enhances the tracking process while ensuring a safe tracking interval.This approach enables the trailing train to operate more comfortably,energy-efficiently and punctually,aligning with passenger needs and industry trends.The method offers valuable insights for optimizing the high-speed train tracking process.
基金Supported by National Natural Science Foundation of China(Grant Nos.U22A20246,52372382)Hefei Municipal Natural Science Foundation(Grant No.2022008)+1 种基金the Open Fund of State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures(Grant No.KF2023-06)S&T Program of Hebei(Grant No.225676162GH).
文摘In the parallel steering coordination control strategy for path tracking,it is difficult to match the current driver steering model using the fixed parameters with the actual driver,and the designed steering coordination control strategy under a single objective and simple conditions is difficult to adapt to the multi-dimensional state variables’input.In this paper,we propose a deep reinforcement learning algorithm-based multi-objective parallel human-machine steering coordination strategy for path tracking considering driver misoperation and external disturbance.Firstly,the driver steering mathematical model is constructed based on the driver preview characteristics and steering delay response,and the driver characteristic parameters are fitted after collecting the actual driver driving data.Secondly,considering that the vehicle is susceptible to the influence of external disturbances during the driving process,the Tube MPC(Tube Model Predictive Control)based path tracking steering controller is designed based on the vehicle system dynamics error model.After verifying that the driver steering model meets the driver steering operation characteristics,DQN(Deep Q-network),DDPG(Deep Deterministic Policy Gradient)and TD3(Twin Delayed Deep Deterministic Policy Gradient)deep reinforcement learning algorithms are utilized to design a multi-objective parallel steering coordination strategy which satisfies the multi-dimensional state variables’input of the vehicle.Finally,the tracking accuracy,lateral safety,human-machine conflict and driver steering load evaluation index are designed in different driver operation states and different road environments,and the performance of the parallel steering coordination control strategies with different deep reinforcement learning algorithms and fuzzy algorithms are compared by simulations and hardware in the loop experiments.The results show that the parallel steering collaborative strategy based on a deep reinforcement learning algorithm can more effectively assist the driver in tracking the target path under lateral wind interference and driver misoperation,and the TD3-based coordination control strategy has better overall performance.
文摘Background With the rapid development of Web3D, virtual reality, and digital twins, virtual trajectories and decision data considerably rely on the analysis and understanding of real video data, particularly in emergency evacuation scenarios. Correctly and effectively evacuating crowds in virtual emergency scenarios are becoming increasingly urgent. One good solution is to extract pedestrian trajectories from videos of emergency situations using a multi-target tracking algorithm and use them to define evacuation procedures. Methods To implement this solution, a trajectory extraction and optimization framework based on multi-target tracking is developed in this study. First, a multi-target tracking algorithm is used to extract and preprocess the trajectory data of the crowd in a video. Then, the trajectory is optimized by combining the trajectory point extraction algorithm and Savitzky-Golay smoothing filtering method. Finally, related experiments are conducted, and the results show that the proposed approach can effectively and accurately extract the trajectories of multiple target objects in real time. Results In addition, the proposed approach retains the real characteristics of the trajectories as much as possible while improving the trajectory smoothing index, which can provide data support for the analysis of pedestrian trajectory data and formulation of personnel evacuation schemes in emergency scenarios. Conclusions Further comparisons with methods used in related studies confirm the feasibility and superiority of the proposed framework.
基金Supported by the National Natural Science Foundation of China(No.90820302,60805027)the Research Fund for Doctoral Program of Higher Education(No.200805330005)the Academician Foundation of Hunan(No.2009FJ4030)
文摘Target tracking in wireless sensor network usually schedules a subset of sensor nodes to constitute a tasking cluster to collaboratively track a target.For the goals of saving energy consumption,prolonging network lifetime and improving tracking accuracy,sensor node scheduling for target tracking is indeed a multi-objective optimization problem.In this paper,a multi-objective optimization sensor node scheduling algorithm is proposed.It employs the unscented Kalman filtering algorithm for target state estimation and establishes tracking accuracy index,predicts the energy consumption of candidate sensor nodes,analyzes the relationship between network lifetime and remaining energy balance so as to construct energy efficiency index.Simulation results show that,compared with the existing sensor node scheduling,our proposed algorithm can achieve superior tracking accuracy and energy efficiency.
基金supported by the National Natural Science Foundation of China(Nos.51775021,52302511)the Fundamental Research Funds for the Central Universities,China(Nos.501JCGG2024129003,501JCGG2024129005,501JCGG2024129006),the Fundamental Research Funds for the Central Universities,China(No.YWF-24-JC-09)the National Key Research and Development Program of China(No.2018YFC1506401)。
文摘This paper studies the tracking control problem for stratospheric airships with userspecified performance.Dealing with the infinite gain phenomenon in the prescribed-time stability,a new stability criterion with bounded gain is proposed by using a new time-varying scaling function.Moreover,a same-side performance function and a novel barrier Lyapunov function are incorporated into the control algorithm,which can compress the feasible domain of tracking error to minimize the overshoot and solve the difficult in tracking error not converging to zero simultaneously.The proposed scheme guarantees the airship capable of operating autonomously with satisfactory transient performance and tracking accuracy,where the performance parameters can be designed artificially and link to the physical process directly.Finally,the effectiveness of the proposed control scheme is verified by theoretical analysis and numerical simulation.
基金supported by the National Natural Science Foundation of China(12072090).
文摘This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles.In order to increase the control amount,this online control legislation makes use of model predictive control(MPC)that is based on the concept of iterative learning control(ILC).By using offline data to decrease the linearized model’s faults,the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed.An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree.During the derivation process,a linearized model of longitudinal dynamics is established.The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.
基金Supported by National Key Research and Development Program of China(Grant No.2021YFB1600402)National Natural Science Foundation of China(Grant No.52072212)+1 种基金Dongfeng USharing Technology Co.,Ltd.,China Intelli‑gent and Connected Vehicles(Beijing)Research Institute Co.,Ltd.“Shuimu Tsinghua Scholarship”of Tsinghua University of China.
文摘Environment perception is one of the most critical technology of intelligent transportation systems(ITS).Motion interaction between multiple vehicles in ITS makes it important to perform multi-object tracking(MOT).However,most existing MOT algorithms follow the tracking-by-detection framework,which separates detection and tracking into two independent segments and limit the global efciency.Recently,a few algorithms have combined feature extraction into one network;however,the tracking portion continues to rely on data association,and requires com‑plex post-processing for life cycle management.Those methods do not combine detection and tracking efciently.This paper presents a novel network to realize joint multi-object detection and tracking in an end-to-end manner for ITS,named as global correlation network(GCNet).Unlike most object detection methods,GCNet introduces a global correlation layer for regression of absolute size and coordinates of bounding boxes,instead of ofsetting predictions.The pipeline of detection and tracking in GCNet is conceptually simple,and does not require compli‑cated tracking strategies such as non-maximum suppression and data association.GCNet was evaluated on a multivehicle tracking dataset,UA-DETRAC,demonstrating promising performance compared to state-of-the-art detectors and trackers.
