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
Advancements in animal behavior quantification methods have driven the development of computational ethology,enabling fully automated behavior analysis.Existing multianimal pose estimation workflows rely on tracking-b...Advancements in animal behavior quantification methods have driven the development of computational ethology,enabling fully automated behavior analysis.Existing multianimal pose estimation workflows rely on tracking-bydetection frameworks for either bottom-up or top-down approaches,requiring retraining to accommodate diverse animal appearances.This study introduces InteBOMB,an integrated workflow that enhances top-down approaches by incorporating generic object tracking,eliminating the need for prior knowledge of target animals while maintaining broad generalizability.InteBOMB includes two key strategies for tracking and segmentation in laboratory environments and two techniques for pose estimation in natural settings.The“background enhancement”strategy optimizesforeground-backgroundcontrastiveloss,generating more discriminative correlation maps.The“online proofreading”strategy stores human-in-the-loop long-term memory and dynamic short-term memory,enabling adaptive updates to object visual features.The“automated labeling suggestion”technique reuses the visual features saved during tracking to identify representative frames for training set labeling.Additionally,the“joint behavior analysis”technique integrates these features with multimodal data,expanding the latent space for behavior classification and clustering.To evaluate the framework,six datasets of mice and six datasets of nonhuman primates were compiled,covering laboratory and natural scenes.Benchmarking results demonstrated a24%improvement in zero-shot generic tracking and a 21%enhancement in joint latent space performance across datasets,highlighting the effectiveness of this approach in robust,generalizable behavior analysis.展开更多
Crack length measurement algorithms based on computer vision have shown promising engineering application prospects in the field of aircraft fatigue crack monitoring.However,due to the complexity of the monitoring env...Crack length measurement algorithms based on computer vision have shown promising engineering application prospects in the field of aircraft fatigue crack monitoring.However,due to the complexity of the monitoring environment,the subtle visual features of small fatigue cracks,and the impact of structural elastic deformation,directly applying object segmentation algorithms often results in significant measurement errors.Therefore,this paper proposes a high-precision crack length measurement method based on Bidirectional Target Tracking Model(Bi2TM),which integrates crack tip localization,interference identification,and length compensation.First,a general object segmentation model is used to perform rough crack segmentation.Then,the Bi2TM network,combined with the visual features of the structure in different stress states,is employed to track the bidirectional position of the crack tip in the“open”and“closed”states.This ultimately enables interference identification within the rough segmented crack region,achieving highprecision length measurement.In a high-interference environment of aircraft fatigue testing,the proposed method is used to measure 1000 crack images ranging from 1 mm to 11 mm.For more than 90%of the samples,the measurement error is less than 5 pixels,demonstrating significant advantages over the existing methods.展开更多
Visual object tracking(VOT),aiming to track a target object in a continuous video,is a fundamental and critical task in computer vision.However,the reliance on third-party resources(e.g.,dataset)for training poses con...Visual object tracking(VOT),aiming to track a target object in a continuous video,is a fundamental and critical task in computer vision.However,the reliance on third-party resources(e.g.,dataset)for training poses concealed threats to the security of VOT models.In this paper,we reveal that VOT models are vulnerable to a poison-only and targeted backdoor attack,where the adversary can achieve arbitrary tracking predictions by manipulating only part of the training data.Specifically,we first define and formulate three different variants of the targeted attacks:size-manipulation,trajectory-manipulation,and hybrid attacks.To implement these,we introduce Random Video Poisoning(RVP),a novel poison-only strategy that exploits temporal correlations within video data by poisoning entire video sequences.Extensive experiments demonstrate that RVP effectively injects controllable backdoors,enabling precise manipulation of tracking behavior upon trigger activation,while maintaining high performance on benign data,thus ensuring stealth.Our findings not only expose significant vulnerabilities but also highlight that the underlying principles could be adapted for beneficial uses,such as dataset watermarking for copyright protection.展开更多
To improve small object detection and trajectory estimation from an aerial moving perspective,we propose the Aerial View Attention-PRB(AVA-PRB)model.AVA-PRB integrates two attention mechanisms—Coordinate Attention(CA...To improve small object detection and trajectory estimation from an aerial moving perspective,we propose the Aerial View Attention-PRB(AVA-PRB)model.AVA-PRB integrates two attention mechanisms—Coordinate Attention(CA)and the Convolutional Block Attention Module(CBAM)—to enhance detection accuracy.Additionally,Shape-IoU is employed as the loss function to refine localization precision.Our model further incorporates an adaptive feature fusion mechanism,which optimizes multi-scale object representation,ensuring robust tracking in complex aerial environments.We evaluate the performance of AVA-PRB on two benchmark datasets:Aerial Person Detection and VisDrone2019-Det.The model achieves 60.9%mAP@0.5 on the Aerial Person Detection dataset,and 51.2%mAP@0.5 on VisDrone2019-Det,demonstrating its effectiveness in aerial object detection.Beyond detection,we propose a novel trajectory estimation method that improves movement path prediction under aerial motion.Experimental results indicate that our approach reduces path deviation by up to 64%,effectively mitigating errors caused by rapid camera movements and background variations.By optimizing feature extraction and enhancing spatialtemporal coherence,our method significantly improves object tracking under aerial moving perspectives.This research addresses the limitations of fixed-camera tracking,enhancing flexibility and accuracy in aerial tracking applications.The proposed approach has broad potential for real-world applications,including surveillance,traffic monitoring,and environmental observation.展开更多
An improved estimation of motion vectors of feature points is proposed for tracking moving objects of dynamic image sequence. Feature points are firstly extracted by the improved minimum intensity change （MIC） algor...An improved estimation of motion vectors of feature points is proposed for tracking moving objects of dynamic image sequence. Feature points are firstly extracted by the improved minimum intensity change （MIC） algorithm. The matching points of these feature points are then determined by adaptive rood pattern searching. Based on the random sample consensus （RANSAC） method, the background motion is finally compensated by the parameters of an affine transform of the background motion. With reasonable morphological filtering, the moving objects are completely extracted from the background, and then tracked accurately. Experimental results show that the improved method is successful on the motion background compensation and offers great promise in tracking moving objects of the dynamic image sequence.展开更多
In practical application,mean shift tracking algorithm is easy to generate tracking drift when the target and the background have similar color distribution.Based on the mean shift algorithm,a kind of background weake...In practical application,mean shift tracking algorithm is easy to generate tracking drift when the target and the background have similar color distribution.Based on the mean shift algorithm,a kind of background weaken weight is proposed in the paper firstly.Combining with the object center weight based on the kernel function,the problem of interference of the similar color background can be solved.And then,a model updating strategy is presented to improve the tracking robustness on the influence of occlusion,illumination,deformation and so on.With the test on the sequence of Tiger,the proposed approach provides better performance than the original mean shift tracking algorithm.展开更多
Recently,deep learning has been widely utilized for object tracking tasks.However,deep learning encounters limits in tasks such as Autonomous Aerial Refueling(AAR),where the target object can vary substantially in siz...Recently,deep learning has been widely utilized for object tracking tasks.However,deep learning encounters limits in tasks such as Autonomous Aerial Refueling(AAR),where the target object can vary substantially in size,requiring high-precision real-time performance in embedded systems.This paper presents a novel embedded adaptiveness single-object tracking framework based on an improved YOLOv4 detection approach and an n-fold Bernoulli probability theorem.First,an Asymmetric Convolutional Network(ACNet)and dense blocks are combined with the YOLOv4 architecture to detect small objects with high precision when similar objects are in the background.The prior object information,such as its location in the previous frame and its speed,is utilized to adaptively track objects of various sizes.Moreover,based on the n-fold Bernoulli probability theorem,we develop a filter that uses statistical laws to reduce the false positive rate of object tracking.To evaluate the efficiency of our algorithm,a new AAR dataset is collected,and extensive AAR detection and tracking experiments are performed.The results demonstrate that our improved detection algorithm is better than the original YOLOv4 algorithm on small and similar object detection tasks;the object tracking algorithm is better than state-of-the-art object tracking algorithms on refueling drogue tracking tasks.展开更多
There are two main trends in the development of unmanned aerial vehicle(UAV)technologies:miniaturization and intellectualization,in which realizing object tracking capabilities for a nano-scale UAV is one of the most ...There are two main trends in the development of unmanned aerial vehicle(UAV)technologies:miniaturization and intellectualization,in which realizing object tracking capabilities for a nano-scale UAV is one of the most challenging problems.In this paper,we present a visual object tracking and servoing control system utilizing a tailor-made 38 g nano-scale quadrotor.A lightweight visual module is integrated to enable object tracking capabilities,and a micro positioning deck is mounted to provide accurate pose estimation.In order to be robust against object appearance variations,a novel object tracking algorithm,denoted by RMCTer,is proposed,which integrates a powerful short-term tracking module and an efficient long-term processing module.In particular,the long-term processing module can provide additional object information and modify the short-term tracking model in a timely manner.Furthermore,a positionbased visual servoing control method is proposed for the quadrotor,where an adaptive tracking controller is designed by leveraging backstepping and adaptive techniques.Stable and accurate object tracking is achieved even under disturbances.Experimental results are presented to demonstrate the high accuracy and stability of the whole tracking system.展开更多
Collaborative Robotics is one of the high-interest research topics in the area of academia and industry.It has been progressively utilized in numerous applications,particularly in intelligent surveillance systems.It a...Collaborative Robotics is one of the high-interest research topics in the area of academia and industry.It has been progressively utilized in numerous applications,particularly in intelligent surveillance systems.It allows the deployment of smart cameras or optical sensors with computer vision techniques,which may serve in several object detection and tracking tasks.These tasks have been considered challenging and high-level perceptual problems,frequently dominated by relative information about the environment,where main concerns such as occlusion,illumination,background,object deformation,and object class variations are commonplace.In order to show the importance of top view surveillance,a collaborative robotics framework has been presented.It can assist in the detection and tracking of multiple objects in top view surveillance.The framework consists of a smart robotic camera embedded with the visual processing unit.The existing pre-trained deep learning models named SSD and YOLO has been adopted for object detection and localization.The detection models are further combined with different tracking algorithms,including GOTURN,MEDIANFLOW,TLD,KCF,MIL,and BOOSTING.These algorithms,along with detection models,help to track and predict the trajectories of detected objects.The pre-trained models are employed;therefore,the generalization performance is also investigated through testing the models on various sequences of top view data set.The detection models achieved maximum True Detection Rate 93%to 90%with a maximum 0.6%False Detection Rate.The tracking results of different algorithms are nearly identical,with tracking accuracy ranging from 90%to 94%.Furthermore,a discussion has been carried out on output results along with future guidelines.展开更多
This paper describes a new framework for object detection and tracking of AUV including underwater acoustic data interpolation, underwater acoustic images segmentation and underwater objects tracking. This framework i...This paper describes a new framework for object detection and tracking of AUV including underwater acoustic data interpolation, underwater acoustic images segmentation and underwater objects tracking. This framework is applied to the design of vision-based method for AUV based on the forward looking sonar sensor. First, the real-time data flow （underwater acoustic images） is pre-processed to form the whole underwater acoustic image, and the relevant position information of objects is extracted and determined. An improved method of double threshold segmentation is proposed to resolve the problem that the threshold cannot be adjusted adaptively in the traditional method. Second, a representation of region information is created in light of the Gaussian particle filter. The weighted integration strategy combining the area and invariant moment is proposed to perfect the weight of particles and to enhance the tracking robustness. Results obtained on the real acoustic vision platform of AUV during sea trials are displayed and discussed. They show that the proposed method can detect and track the moving objects underwater online, and it is effective and robust.展开更多
Inspired by human behaviors, a robot object tracking model is proposed on the basis of visual attention mechanism, which is fit for the theory of topological perception. The model integrates the image-driven, bottom-u...Inspired by human behaviors, a robot object tracking model is proposed on the basis of visual attention mechanism, which is fit for the theory of topological perception. The model integrates the image-driven, bottom-up attention and the object-driven, top-down attention, whereas the previous attention model has mostly focused on either the bottom-up or top-down attention. By the bottom-up component, the whole scene is segmented into the ground region and the salient regions. Guided by top-down strategy which is achieved by a topological graph, the object regions are separated from the salient regions. The salient regions except the object regions are the barrier regions. In order to estimate the model, a mobile robot platform is developed, on which some experiments are implemented. The experimental results indicate that processing an image with a resolution of 752 × 480 pixels takes less than 200 ms and the object regions are unabridged. The analysis obtained by comparing the proposed model with the existing model demonstrates that the proposed model has some advantages in robot object tracking in terms of speed and efficiency.展开更多
A method for moving object recognition and tracking in the intelligent traffic monitoring system is presented. For the shortcomings and deficiencies of the frame-subtraction method, a redundant discrete wavelet transf...A method for moving object recognition and tracking in the intelligent traffic monitoring system is presented. For the shortcomings and deficiencies of the frame-subtraction method, a redundant discrete wavelet transform （RDWT） based moving object recognition algorithm is put forward, which directly detects moving objects in the redundant discrete wavelet transform domain. An improved adaptive mean-shift algorithm is used to track the moving object in the follow up frames. Experimental results show that the algorithm can effectively extract the moving object, even though the object is similar to the background, and the results are better than the traditional frame-subtraction method. The object tracking is accurate without the impact of changes in the size of the object. Therefore the algorithm has a certain practical value and prospect.展开更多
This paper addresses the problem of visual object tracking for Unmanned Aerial Vehicles(UAVs).Most Siamese trackers are used to regard object tracking as classification and regression problems.However,it is difficult ...This paper addresses the problem of visual object tracking for Unmanned Aerial Vehicles(UAVs).Most Siamese trackers are used to regard object tracking as classification and regression problems.However,it is difficult for these trackers to accurately classify in the face of similar objects,background clutters and other common challenges in UAV scenes.So,a reliable classifier is the key to improving UAV tracking performance.In this paper,a simple yet efficient tracker following the basic architecture of the Siamese neural network is proposed,which improves the classification ability from three stages.First,the frequency channel attention module is introduced to enhance the target features via frequency domain learning.Second,a template-guided attention module is designed to promote information exchange between the template branch and the search branch,which can get reliable classification response maps.Third,adaptive cross-entropy loss is proposed to make the tracker focus on hard samples that contribute more to the training process,solving the data imbalance between positive and negative samples.To evaluate the performance of the proposed tracker,comprehensive experiments are conducted on two challenging aerial datasets,including UAV123 and UAVDT.Experimental results demonstrate that the proposed tracker achieves favorable tracking performances in aerial benchmarks beyond 41 frames/s.We conducted experiments in real UAV scenes to further verify the efficiency of our tracker in the real world.展开更多
In this paper,we provide a new approach for intelligent traffic transportation in the intelligent vehicular networks,which aims at collecting the vehicles’locations,trajectories and other key driving parameters for t...In this paper,we provide a new approach for intelligent traffic transportation in the intelligent vehicular networks,which aims at collecting the vehicles’locations,trajectories and other key driving parameters for the time-critical autonomous driving’s requirement.The key of our method is a multi-vehicle tracking framework in the traffic monitoring scenario..Our proposed framework is composed of three modules:multi-vehicle detection,multi-vehicle association and miss-detected vehicle tracking.For the first module,we integrate self-attention mechanism into detector of using key point estimation for better detection effect.For the second module,we apply the multi-dimensional information for robustness promotion,including vehicle re-identification(Re-ID)features,historical trajectory information,and spatial position information For the third module,we re-track the miss-detected vehicles with occlusions in the first detection module.Besides,we utilize the asymmetric convolution and depth-wise separable convolution to reduce the model’s parameters for speed-up.Extensive experimental results show the effectiveness of our proposed multi-vehicle tracking framework.展开更多
In dense pedestrian tracking,frequent object occlusions and close distances between objects cause difficulty when accurately estimating object trajectories.In this study,a conditional random field tracking model is es...In dense pedestrian tracking,frequent object occlusions and close distances between objects cause difficulty when accurately estimating object trajectories.In this study,a conditional random field tracking model is established by using a visual long short term memory network in the three-dimensional(3D)space and the motion estimations jointly performed on object trajectory segments.Object visual field information is added to the long short term memory network to improve the accuracy of the motion related object pair selection and motion estimation.To address the uncertainty of the length and interval of trajectory segments,a multimode long short term memory network is proposed for the object motion estimation.The tracking performance is evaluated using the PETS2009 dataset.The experimental results show that the proposed method achieves better performance than the tracking methods based on the independent motion estimation.展开更多
An object model-based tracking method is useful for tracking multiple objects, but the main difficulties are modeling objects reliably and tracking objects via models in successive frames. An effective tracking method...An object model-based tracking method is useful for tracking multiple objects, but the main difficulties are modeling objects reliably and tracking objects via models in successive frames. An effective tracking method using the object models is proposed to track multiple objects in a real-time visual surveillance system. Firstly, for detecting objects, an adaptive kernel density estimation method is utilized, which uses an adaptive bandwidth and features combining colour and gradient. Secondly, some models of objects are built for describing motion, shape and colour features. Then, a matching matrix is formed to analyze tracking situations. If objects are tracked under occlusions, the optimal ＂visual＂ object is found to represent the occluded object, and the posterior probability of pixel is used to determine which pixel is utilized for updating object models. Extensive experiments show that this method improves the accuracy and validity of tracking objects even under occlusions and is used in real-time visual surveillance systems.展开更多
Unmanned aerial vehicles(UAVs)can be used to monitor traffic in a variety of settings,including security,traffic surveillance,and traffic control.Numerous academics have been drawn to this topic because of the challen...Unmanned aerial vehicles(UAVs)can be used to monitor traffic in a variety of settings,including security,traffic surveillance,and traffic control.Numerous academics have been drawn to this topic because of the challenges and the large variety of applications.This paper proposes a new and efficient vehicle detection and tracking system that is based on road extraction and identifying objects on it.It is inspired by existing detection systems that comprise stationary data collectors such as induction loops and stationary cameras that have a limited field of view and are not mobile.The goal of this study is to develop a method that first extracts the region of interest(ROI),then finds and tracks the items of interest.The suggested system is divided into six stages.The photos from the obtained dataset are appropriately georeferenced to their actual locations in the first phase,after which they are all co-registered.The ROI,or road and its objects,are retrieved using the GrabCut method in the second phase.The third phase entails data preparation.The segmented images’noise is eliminated using Gaussian blur,after which the images are changed to grayscale and forwarded to the following stage for additional morphological procedures.The YOLOv3 algorithm is used in the fourth step to find any automobiles in the photos.Following that,the Kalman filter and centroid tracking are used to perform the tracking of the detected cars.The Lucas-Kanade method is then used to perform the trajectory analysis on the vehicles.The suggested model is put to the test and assessed using the Vehicle Aerial Imaging from Drone(VAID)dataset.For detection and tracking,the model was able to attain accuracy levels of 96.7%and 91.6%,respectively.展开更多
Single object tracking based on deep learning has achieved the advanced performance in many applications of computer vision.However,the existing trackers have certain limitations owing to deformation,occlusion,movemen...Single object tracking based on deep learning has achieved the advanced performance in many applications of computer vision.However,the existing trackers have certain limitations owing to deformation,occlusion,movement and some other conditions.We propose a siamese attentional dense network called SiamADN in an end-to-end offline manner,especially aiming at unmanned aerial vehicle(UAV)tracking.First,it applies a dense network to reduce vanishing-gradient,which strengthens the features transfer.Second,the channel attention mechanism is involved into the Densenet structure,in order to focus on the possible key regions.The advance corner detection network is introduced to improve the following tracking process.Extensive experiments are carried out on four mainly tracking benchmarks as OTB-2015,UAV123,LaSOT and VOT.The accuracy rate on UAV123 is 78.9%,and the running speed is 32 frame per second(FPS),which demonstrates its efficiency in the practical real application.展开更多
Video object tracking is an important research topic of computer vision, whichfinds a wide range of applications in video surveillance, robotics, human-computerinteraction and so on. Although many moving object tracki...Video object tracking is an important research topic of computer vision, whichfinds a wide range of applications in video surveillance, robotics, human-computerinteraction and so on. Although many moving object tracking algorithms have beenproposed, there are still many difficulties in the actual tracking process, such asillumination change, occlusion, motion blurring, scale change, self-change and so on.Therefore, the development of object tracking technology is still challenging. Theemergence of deep learning theory and method provides a new opportunity for theresearch of object tracking, and it is also the main theoretical framework for the researchof moving object tracking algorithm in this paper. In this paper, the existing deeptracking-based target tracking algorithms are classified and sorted out. Based on theprevious knowledge and my own understanding, several solutions are proposed for theexisting methods. In addition, the existing deep learning target tracking method is stilldifficult to meet the requirements of real-time, how to design the network and trackingprocess to achieve speed and effect improvement, there is still a lot of research space.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supported by the STI 2030-Major Projects(2022ZD0211900,2022ZD0211902)STI 2030-Major Projects(2021ZD0204500,2021ZD0204503)+1 种基金National Natural Science Foundation of China(32171461)National Key Research and Development Program of China(2023YFC3208303)。
文摘Advancements in animal behavior quantification methods have driven the development of computational ethology,enabling fully automated behavior analysis.Existing multianimal pose estimation workflows rely on tracking-bydetection frameworks for either bottom-up or top-down approaches,requiring retraining to accommodate diverse animal appearances.This study introduces InteBOMB,an integrated workflow that enhances top-down approaches by incorporating generic object tracking,eliminating the need for prior knowledge of target animals while maintaining broad generalizability.InteBOMB includes two key strategies for tracking and segmentation in laboratory environments and two techniques for pose estimation in natural settings.The“background enhancement”strategy optimizesforeground-backgroundcontrastiveloss,generating more discriminative correlation maps.The“online proofreading”strategy stores human-in-the-loop long-term memory and dynamic short-term memory,enabling adaptive updates to object visual features.The“automated labeling suggestion”technique reuses the visual features saved during tracking to identify representative frames for training set labeling.Additionally,the“joint behavior analysis”technique integrates these features with multimodal data,expanding the latent space for behavior classification and clustering.To evaluate the framework,six datasets of mice and six datasets of nonhuman primates were compiled,covering laboratory and natural scenes.Benchmarking results demonstrated a24%improvement in zero-shot generic tracking and a 21%enhancement in joint latent space performance across datasets,highlighting the effectiveness of this approach in robust,generalizable behavior analysis.
基金supported by the New Cornerstone Science Foundation through the XPLORER PRIZE,China(No.XPLORER-2024-1036)the independent research project of the National Key Laboratory of Strength and Structural Integrity,China(No.BYST-QZSYS-24-072-5)。
文摘Crack length measurement algorithms based on computer vision have shown promising engineering application prospects in the field of aircraft fatigue crack monitoring.However,due to the complexity of the monitoring environment,the subtle visual features of small fatigue cracks,and the impact of structural elastic deformation,directly applying object segmentation algorithms often results in significant measurement errors.Therefore,this paper proposes a high-precision crack length measurement method based on Bidirectional Target Tracking Model(Bi2TM),which integrates crack tip localization,interference identification,and length compensation.First,a general object segmentation model is used to perform rough crack segmentation.Then,the Bi2TM network,combined with the visual features of the structure in different stress states,is employed to track the bidirectional position of the crack tip in the“open”and“closed”states.This ultimately enables interference identification within the rough segmented crack region,achieving highprecision length measurement.In a high-interference environment of aircraft fatigue testing,the proposed method is used to measure 1000 crack images ranging from 1 mm to 11 mm.For more than 90%of the samples,the measurement error is less than 5 pixels,demonstrating significant advantages over the existing methods.
基金supported in part by the"Pioneer"and"Leading Goose"R&D Program of Zhejiang under Grant No. 2024C01169the National Natural Science Foundation of China under Grant Nos. 62441238 and U2441240。
文摘Visual object tracking(VOT),aiming to track a target object in a continuous video,is a fundamental and critical task in computer vision.However,the reliance on third-party resources(e.g.,dataset)for training poses concealed threats to the security of VOT models.In this paper,we reveal that VOT models are vulnerable to a poison-only and targeted backdoor attack,where the adversary can achieve arbitrary tracking predictions by manipulating only part of the training data.Specifically,we first define and formulate three different variants of the targeted attacks:size-manipulation,trajectory-manipulation,and hybrid attacks.To implement these,we introduce Random Video Poisoning(RVP),a novel poison-only strategy that exploits temporal correlations within video data by poisoning entire video sequences.Extensive experiments demonstrate that RVP effectively injects controllable backdoors,enabling precise manipulation of tracking behavior upon trigger activation,while maintaining high performance on benign data,thus ensuring stealth.Our findings not only expose significant vulnerabilities but also highlight that the underlying principles could be adapted for beneficial uses,such as dataset watermarking for copyright protection.
基金funded by theNational Science and TechnologyCouncil(NSTC),Taiwan,under grant numbers NSTC 113-2634-F-A49-007 and NSTC 112-2634-F-A49-007.
文摘To improve small object detection and trajectory estimation from an aerial moving perspective,we propose the Aerial View Attention-PRB(AVA-PRB)model.AVA-PRB integrates two attention mechanisms—Coordinate Attention(CA)and the Convolutional Block Attention Module(CBAM)—to enhance detection accuracy.Additionally,Shape-IoU is employed as the loss function to refine localization precision.Our model further incorporates an adaptive feature fusion mechanism,which optimizes multi-scale object representation,ensuring robust tracking in complex aerial environments.We evaluate the performance of AVA-PRB on two benchmark datasets:Aerial Person Detection and VisDrone2019-Det.The model achieves 60.9%mAP@0.5 on the Aerial Person Detection dataset,and 51.2%mAP@0.5 on VisDrone2019-Det,demonstrating its effectiveness in aerial object detection.Beyond detection,we propose a novel trajectory estimation method that improves movement path prediction under aerial motion.Experimental results indicate that our approach reduces path deviation by up to 64%,effectively mitigating errors caused by rapid camera movements and background variations.By optimizing feature extraction and enhancing spatialtemporal coherence,our method significantly improves object tracking under aerial moving perspectives.This research addresses the limitations of fixed-camera tracking,enhancing flexibility and accuracy in aerial tracking applications.The proposed approach has broad potential for real-world applications,including surveillance,traffic monitoring,and environmental observation.
文摘An improved estimation of motion vectors of feature points is proposed for tracking moving objects of dynamic image sequence. Feature points are firstly extracted by the improved minimum intensity change （MIC） algorithm. The matching points of these feature points are then determined by adaptive rood pattern searching. Based on the random sample consensus （RANSAC） method, the background motion is finally compensated by the parameters of an affine transform of the background motion. With reasonable morphological filtering, the moving objects are completely extracted from the background, and then tracked accurately. Experimental results show that the improved method is successful on the motion background compensation and offers great promise in tracking moving objects of the dynamic image sequence.
基金National Natural Science Foundation of China(No.61201412)
文摘In practical application,mean shift tracking algorithm is easy to generate tracking drift when the target and the background have similar color distribution.Based on the mean shift algorithm,a kind of background weaken weight is proposed in the paper firstly.Combining with the object center weight based on the kernel function,the problem of interference of the similar color background can be solved.And then,a model updating strategy is presented to improve the tracking robustness on the influence of occlusion,illumination,deformation and so on.With the test on the sequence of Tiger,the proposed approach provides better performance than the original mean shift tracking algorithm.
文摘Recently,deep learning has been widely utilized for object tracking tasks.However,deep learning encounters limits in tasks such as Autonomous Aerial Refueling(AAR),where the target object can vary substantially in size,requiring high-precision real-time performance in embedded systems.This paper presents a novel embedded adaptiveness single-object tracking framework based on an improved YOLOv4 detection approach and an n-fold Bernoulli probability theorem.First,an Asymmetric Convolutional Network(ACNet)and dense blocks are combined with the YOLOv4 architecture to detect small objects with high precision when similar objects are in the background.The prior object information,such as its location in the previous frame and its speed,is utilized to adaptively track objects of various sizes.Moreover,based on the n-fold Bernoulli probability theorem,we develop a filter that uses statistical laws to reduce the false positive rate of object tracking.To evaluate the efficiency of our algorithm,a new AAR dataset is collected,and extensive AAR detection and tracking experiments are performed.The results demonstrate that our improved detection algorithm is better than the original YOLOv4 algorithm on small and similar object detection tasks;the object tracking algorithm is better than state-of-the-art object tracking algorithms on refueling drogue tracking tasks.
基金supported in part by the Institute for Guo Qiang of Tsinghua University(2019GQG1023)in part by Graduate Education and Teaching Reform Project of Tsinghua University(202007J007)+1 种基金in part by National Natural Science Foundation of China(U19B2029,62073028,61803222)in part by the Independent Research Program of Tsinghua University(2018Z05JDX002)。
文摘There are two main trends in the development of unmanned aerial vehicle(UAV)technologies:miniaturization and intellectualization,in which realizing object tracking capabilities for a nano-scale UAV is one of the most challenging problems.In this paper,we present a visual object tracking and servoing control system utilizing a tailor-made 38 g nano-scale quadrotor.A lightweight visual module is integrated to enable object tracking capabilities,and a micro positioning deck is mounted to provide accurate pose estimation.In order to be robust against object appearance variations,a novel object tracking algorithm,denoted by RMCTer,is proposed,which integrates a powerful short-term tracking module and an efficient long-term processing module.In particular,the long-term processing module can provide additional object information and modify the short-term tracking model in a timely manner.Furthermore,a positionbased visual servoing control method is proposed for the quadrotor,where an adaptive tracking controller is designed by leveraging backstepping and adaptive techniques.Stable and accurate object tracking is achieved even under disturbances.Experimental results are presented to demonstrate the high accuracy and stability of the whole tracking system.
基金the Framework of International Cooperation Program managed by the National Research Foundation of Korea(2019K1A3A1A8011295711).
文摘Collaborative Robotics is one of the high-interest research topics in the area of academia and industry.It has been progressively utilized in numerous applications,particularly in intelligent surveillance systems.It allows the deployment of smart cameras or optical sensors with computer vision techniques,which may serve in several object detection and tracking tasks.These tasks have been considered challenging and high-level perceptual problems,frequently dominated by relative information about the environment,where main concerns such as occlusion,illumination,background,object deformation,and object class variations are commonplace.In order to show the importance of top view surveillance,a collaborative robotics framework has been presented.It can assist in the detection and tracking of multiple objects in top view surveillance.The framework consists of a smart robotic camera embedded with the visual processing unit.The existing pre-trained deep learning models named SSD and YOLO has been adopted for object detection and localization.The detection models are further combined with different tracking algorithms,including GOTURN,MEDIANFLOW,TLD,KCF,MIL,and BOOSTING.These algorithms,along with detection models,help to track and predict the trajectories of detected objects.The pre-trained models are employed;therefore,the generalization performance is also investigated through testing the models on various sequences of top view data set.The detection models achieved maximum True Detection Rate 93%to 90%with a maximum 0.6%False Detection Rate.The tracking results of different algorithms are nearly identical,with tracking accuracy ranging from 90%to 94%.Furthermore,a discussion has been carried out on output results along with future guidelines.
基金supported by the National Natural Science Foundation of China(Grant No.51009040)Heilongjiang Postdoctoral Fund(Grant No.LBH-Z11205)+1 种基金the National High Technology Research and Development Program of China(863 Program,Grant No.2011AA09A106)the China Postdoctoral Science Foundation(Grant No.2012M510928)
文摘This paper describes a new framework for object detection and tracking of AUV including underwater acoustic data interpolation, underwater acoustic images segmentation and underwater objects tracking. This framework is applied to the design of vision-based method for AUV based on the forward looking sonar sensor. First, the real-time data flow （underwater acoustic images） is pre-processed to form the whole underwater acoustic image, and the relevant position information of objects is extracted and determined. An improved method of double threshold segmentation is proposed to resolve the problem that the threshold cannot be adjusted adaptively in the traditional method. Second, a representation of region information is created in light of the Gaussian particle filter. The weighted integration strategy combining the area and invariant moment is proposed to perfect the weight of particles and to enhance the tracking robustness. Results obtained on the real acoustic vision platform of AUV during sea trials are displayed and discussed. They show that the proposed method can detect and track the moving objects underwater online, and it is effective and robust.
基金supported by National Basic Research Program of China (973 Program) (No. 2006CB300407)National Natural Science Foundation of China (No. 50775017)
文摘Inspired by human behaviors, a robot object tracking model is proposed on the basis of visual attention mechanism, which is fit for the theory of topological perception. The model integrates the image-driven, bottom-up attention and the object-driven, top-down attention, whereas the previous attention model has mostly focused on either the bottom-up or top-down attention. By the bottom-up component, the whole scene is segmented into the ground region and the salient regions. Guided by top-down strategy which is achieved by a topological graph, the object regions are separated from the salient regions. The salient regions except the object regions are the barrier regions. In order to estimate the model, a mobile robot platform is developed, on which some experiments are implemented. The experimental results indicate that processing an image with a resolution of 752 × 480 pixels takes less than 200 ms and the object regions are unabridged. The analysis obtained by comparing the proposed model with the existing model demonstrates that the proposed model has some advantages in robot object tracking in terms of speed and efficiency.
文摘A method for moving object recognition and tracking in the intelligent traffic monitoring system is presented. For the shortcomings and deficiencies of the frame-subtraction method, a redundant discrete wavelet transform （RDWT） based moving object recognition algorithm is put forward, which directly detects moving objects in the redundant discrete wavelet transform domain. An improved adaptive mean-shift algorithm is used to track the moving object in the follow up frames. Experimental results show that the algorithm can effectively extract the moving object, even though the object is similar to the background, and the results are better than the traditional frame-subtraction method. The object tracking is accurate without the impact of changes in the size of the object. Therefore the algorithm has a certain practical value and prospect.
基金This study was co-supported by the National Natural Science Foundation of China(Nos.61673017 and 61403398).
文摘This paper addresses the problem of visual object tracking for Unmanned Aerial Vehicles(UAVs).Most Siamese trackers are used to regard object tracking as classification and regression problems.However,it is difficult for these trackers to accurately classify in the face of similar objects,background clutters and other common challenges in UAV scenes.So,a reliable classifier is the key to improving UAV tracking performance.In this paper,a simple yet efficient tracker following the basic architecture of the Siamese neural network is proposed,which improves the classification ability from three stages.First,the frequency channel attention module is introduced to enhance the target features via frequency domain learning.Second,a template-guided attention module is designed to promote information exchange between the template branch and the search branch,which can get reliable classification response maps.Third,adaptive cross-entropy loss is proposed to make the tracker focus on hard samples that contribute more to the training process,solving the data imbalance between positive and negative samples.To evaluate the performance of the proposed tracker,comprehensive experiments are conducted on two challenging aerial datasets,including UAV123 and UAVDT.Experimental results demonstrate that the proposed tracker achieves favorable tracking performances in aerial benchmarks beyond 41 frames/s.We conducted experiments in real UAV scenes to further verify the efficiency of our tracker in the real world.
基金This work was supported in part by the Beijing Natural Science Foundation(L191004)the National Natural Science Foundation of China under No.61720106007 and No.61872047+1 种基金the Beijing Nova Program under No.Z201100006820124the Funds for Cre ative Research Groups of China under No.61921003,and the 111 Project(B18008).
文摘In this paper,we provide a new approach for intelligent traffic transportation in the intelligent vehicular networks,which aims at collecting the vehicles’locations,trajectories and other key driving parameters for the time-critical autonomous driving’s requirement.The key of our method is a multi-vehicle tracking framework in the traffic monitoring scenario..Our proposed framework is composed of three modules:multi-vehicle detection,multi-vehicle association and miss-detected vehicle tracking.For the first module,we integrate self-attention mechanism into detector of using key point estimation for better detection effect.For the second module,we apply the multi-dimensional information for robustness promotion,including vehicle re-identification(Re-ID)features,historical trajectory information,and spatial position information For the third module,we re-track the miss-detected vehicles with occlusions in the first detection module.Besides,we utilize the asymmetric convolution and depth-wise separable convolution to reduce the model’s parameters for speed-up.Extensive experimental results show the effectiveness of our proposed multi-vehicle tracking framework.
文摘In dense pedestrian tracking,frequent object occlusions and close distances between objects cause difficulty when accurately estimating object trajectories.In this study,a conditional random field tracking model is established by using a visual long short term memory network in the three-dimensional(3D)space and the motion estimations jointly performed on object trajectory segments.Object visual field information is added to the long short term memory network to improve the accuracy of the motion related object pair selection and motion estimation.To address the uncertainty of the length and interval of trajectory segments,a multimode long short term memory network is proposed for the object motion estimation.The tracking performance is evaluated using the PETS2009 dataset.The experimental results show that the proposed method achieves better performance than the tracking methods based on the independent motion estimation.
基金supported by the National Natural Science Foundation of China(60835004 60775047+2 种基金 60872130)the National High Technology Research and Development Program of China(863 Program)(2007AA04Z244 2008AA04Z214)
文摘An object model-based tracking method is useful for tracking multiple objects, but the main difficulties are modeling objects reliably and tracking objects via models in successive frames. An effective tracking method using the object models is proposed to track multiple objects in a real-time visual surveillance system. Firstly, for detecting objects, an adaptive kernel density estimation method is utilized, which uses an adaptive bandwidth and features combining colour and gradient. Secondly, some models of objects are built for describing motion, shape and colour features. Then, a matching matrix is formed to analyze tracking situations. If objects are tracked under occlusions, the optimal ＂visual＂ object is found to represent the occluded object, and the posterior probability of pixel is used to determine which pixel is utilized for updating object models. Extensive experiments show that this method improves the accuracy and validity of tracking objects even under occlusions and is used in real-time visual surveillance systems.
基金supported by the MSIT(Ministry of Science and ICT),Korea,under the ICAN(ICT Challenge and Advanced Network of HRD)program(IITP-2023-RS-2022-00156326)supervised by the IITP(Institute of Information&Communications Technology Planning&Evaluation).
文摘Unmanned aerial vehicles(UAVs)can be used to monitor traffic in a variety of settings,including security,traffic surveillance,and traffic control.Numerous academics have been drawn to this topic because of the challenges and the large variety of applications.This paper proposes a new and efficient vehicle detection and tracking system that is based on road extraction and identifying objects on it.It is inspired by existing detection systems that comprise stationary data collectors such as induction loops and stationary cameras that have a limited field of view and are not mobile.The goal of this study is to develop a method that first extracts the region of interest(ROI),then finds and tracks the items of interest.The suggested system is divided into six stages.The photos from the obtained dataset are appropriately georeferenced to their actual locations in the first phase,after which they are all co-registered.The ROI,or road and its objects,are retrieved using the GrabCut method in the second phase.The third phase entails data preparation.The segmented images’noise is eliminated using Gaussian blur,after which the images are changed to grayscale and forwarded to the following stage for additional morphological procedures.The YOLOv3 algorithm is used in the fourth step to find any automobiles in the photos.Following that,the Kalman filter and centroid tracking are used to perform the tracking of the detected cars.The Lucas-Kanade method is then used to perform the trajectory analysis on the vehicles.The suggested model is put to the test and assessed using the Vehicle Aerial Imaging from Drone(VAID)dataset.For detection and tracking,the model was able to attain accuracy levels of 96.7%and 91.6%,respectively.
基金supported by the Zhejiang Key Laboratory of General Aviation Operation Technology(No.JDGA2020-7)the National Natural Science Foundation of China(No.62173237)+3 种基金the Natural Science Foundation of Liaoning Province(No.2019-MS-251)the Talent Project of Revitalization Liaoning Province(No.XLYC1907022)the Key R&D Projects of Liaoning Province(No.2020JH2/10100045)the High-Level Innovation Talent Project of Shenyang(No.RC190030).
文摘Single object tracking based on deep learning has achieved the advanced performance in many applications of computer vision.However,the existing trackers have certain limitations owing to deformation,occlusion,movement and some other conditions.We propose a siamese attentional dense network called SiamADN in an end-to-end offline manner,especially aiming at unmanned aerial vehicle(UAV)tracking.First,it applies a dense network to reduce vanishing-gradient,which strengthens the features transfer.Second,the channel attention mechanism is involved into the Densenet structure,in order to focus on the possible key regions.The advance corner detection network is introduced to improve the following tracking process.Extensive experiments are carried out on four mainly tracking benchmarks as OTB-2015,UAV123,LaSOT and VOT.The accuracy rate on UAV123 is 78.9%,and the running speed is 32 frame per second(FPS),which demonstrates its efficiency in the practical real application.
基金supported by National Natural Science Foundationof China (Grant No. 51874300)the National Natural Science Foundation of China andShanxi Provincial People’s Government Jointly Funded Project of China for Coal Baseand Low Carbon (Grant No. U1510115)+2 种基金National Natural Science Foundation of China(51104157)the Qing Lan Project, the China Postdoctoral Science Foundation (Grant No.2013T60574)the Scientific Instrument Developing Project of the Chinese Academy ofSciences (Grant No. YJKYYQ20170074).
文摘Video object tracking is an important research topic of computer vision, whichfinds a wide range of applications in video surveillance, robotics, human-computerinteraction and so on. Although many moving object tracking algorithms have beenproposed, there are still many difficulties in the actual tracking process, such asillumination change, occlusion, motion blurring, scale change, self-change and so on.Therefore, the development of object tracking technology is still challenging. Theemergence of deep learning theory and method provides a new opportunity for theresearch of object tracking, and it is also the main theoretical framework for the researchof moving object tracking algorithm in this paper. In this paper, the existing deeptracking-based target tracking algorithms are classified and sorted out. Based on theprevious knowledge and my own understanding, several solutions are proposed for theexisting methods. In addition, the existing deep learning target tracking method is stilldifficult to meet the requirements of real-time, how to design the network and trackingprocess to achieve speed and effect improvement, there is still a lot of research space.
