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FishTracker:An Efficient Multi-Object Tracking Algorithm for Fish Monitoring in a RAS Environment 认领 引用
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作者 Yuqiang Wu Zhao Ji +4 位作者 Guanqi You Zihan Zhang Chaoping Lu Huanliang Xu Zhaoyu Zhai 《Computers, Materials & Continua》 SCIE EI 2026年第2期805-826,共22页
Understanding fish movement trajectories in aquaculture is essential for practical applications,such as disease warning,feeding optimization,and breeding management.These trajectories reveal key information about the ... Understanding fish movement trajectories in aquaculture is essential for practical applications,such as disease warning,feeding optimization,and breeding management.These trajectories reveal key information about the fish’s behavior,health,and environmental adaptability.However,when multi-object tracking(MOT)algorithms are applied to the high-density aquaculture environment,occlusion and overlapping among fish may result in missed detections,false detections,and identity switching problems,which limit the tracking accuracy.To address these issues,this paper proposes FishTracker,a MOT algorithm,by utilizing a Tracking-by-Detection framework.First,the neck part of the YOLOv8 model is enhanced by introducing a Multi-Scale Dilated Attention(MSDA)module to improve object localization and classification confidence.Second,an Adaptive Kalman Filter(AKF)is employed in the tracking phase to dynamically adjust motion prediction parameters,thereby overcoming target adhesion and nonlinear motion in complex scenarios.Experimental results show that FishTracker achieves a multi-object tracking accuracy(MOTA)of 93.22% and 87.24% in bright and dark illumination conditions,respectively.Further validation in a real aquaculture scenario reveal that FishTracker achieves aMOTA of 76.70%,which is 5.34% higher than the baselinemodel.The higher order tracking accuracy(HOTA)reaches 50.5%,which is 3.4% higher than the benchmark.In conclusion,FishTracker can provide reliable technical support for accurate tracking and behavioral analysis of high-density fish populations. 展开更多
关键词 Aquaculture multi-object tracking YOLOv8 adaptive Kalman filter attention mechanism
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MDGAN-DIFI: Multi-Object Tracking for USVs Based on Deep Iterative Frame Interpolation and Motion Deblurring Using GAN Model 认领 引用
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作者 Manh-Tuan Ha Nhu-Nghia Bui +1 位作者 Dinh-Quy Vu Thai-Viet Dang 《Computers, Materials & Continua》 SCIE EI 2026年第6期697-720,共24页
In the realm of unmanned surface vehicle(USV)operations,leveraging environmental factors to enhance situational awareness has garnered significant academic attention.Developing vision systems for USVs presents conside... In the realm of unmanned surface vehicle(USV)operations,leveraging environmental factors to enhance situational awareness has garnered significant academic attention.Developing vision systems for USVs presents considerable challenges,mainly due to variable observational conditions and angular vibrations caused by hydrodynamic forces.The paper proposed a novel MDGAN-DIFI network for end-to-end multi-object tracking(MOT),specifically designed for camera systems mounted on USVs.Beyond enhancing traditional MOT models,the proposed MDGAN-DIFI includes preprocessing modules designed to enhance the efficiency of processing input signal quality.Initially,a Deep Iterative Frame Interpolation(DIFI)module is used to stabilize frames in the spatiotemporal domain.Next,an enhanced generative adversarial network(GAN)model is applied to reduce motion blur affecting objects within the field of view.Finally,a YOLO-CSSA architecture combines dual infrared(IR)and RGB data streams to maintain consistent performance across diverse environmental conditions.By synthesizing intermediate frames and restoring blurred details,the framework seeks to stabilize object motion trajectories and recover distinctive appearance features prior to tracking.This approach directly tackles the main causes of tracking failure in maritime environments,such as motion discontinuities and visual degradation.Experimental results demonstrate that the proposed approach outperforms conventional methods in multi-object tracking on USVs,achieving a maximum accuracy(MOTA)of 47.0%and an IDF1 score of 50.1%under challenging operational conditions.Consequently,the proposed multi-object tracking network provides a more robust foundation for subsequent detection and data association processes. 展开更多
关键词 Multi-object tracking unmanned surface vehicles deep interactive frame interpolation motion blur generative adversarial networks(GAN)
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TopoEKF:From State-Space Estimation to Topological Signatures for Enhanced Multi-Object Tracking and Anomaly Detection in UAVs 认领 引用
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作者 Rabia Kıratlı Hatice Unlu Eroğlu Alperen Eroğlu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期860-908,共49页
Reliable multi-object detection and tracking play a critical role in Unmanned Aerial Vehicles-based aerial surveillance applications operating under challenging real-world conditions.This study presents a mathematical... Reliable multi-object detection and tracking play a critical role in Unmanned Aerial Vehicles-based aerial surveillance applications operating under challenging real-world conditions.This study presents a mathematically grounded,model-driven tracking framework named TopoEKF,which integrates an enhanced Adaptive Extended Kalman Filter with Topological Data Analysis to improve both tracking robustness and anomaly detection performance.Unlike prior approaches that primarily focus on refining object detection architectures,this work emphasizes the predictive power of iterative Bayesian filtering,optimal state estimation,and adaptive error minimization within a unified mathematical framework.The proposed system employs a carefully optimized YOLOvl2 detector to provide accurate object location priors,followed by a formally defined discrete-time linear Gaussian tracking model.The Adaptive EKF is leveraged to handle nonlinearities arising from the projection of three-dimensional object motion onto the two-dimensional image plane through local linearization.To further enhance robustness under low resolution,large object-to-image distances,frequent occlusions,and environmental noise,TopoEKF introduces adaptive noise covariance modeling driven by measurement confidence,occlusion status,and topological feedback.Persistent homology is applied to EKF-filtered trajectories to extract topological signatures that characterize the global structure of object motion.These features are transformed into fixed-dimensional representations and processed by an unsupervised Isolation Forest classifier for trajectory-level anomaly detection.Experimental evaluations are conducted on a challenging hybrid dataset combining scenarios from COCO,VisDrone,UAVDT,Road_Anomaly_Dataset,and DoTA benchmarks.Quantitative results demonstrate that TopoEKF improves Multi-Object Tracking Accuracy from 72.8%to 76.3%and reduces identity switches by approximately 34%compared to a standard EKF baseline.The enhanced EKF achieves up to 20%higher robustness in highly noisy and indoor environments while maintaining realtime performance at 28.5 frames per second on resource-constrained embedded platforms.In the anomaly detection stage,the integration of persistent homology-based features improves the F1-score from 66%to 84%,with substantial gains in both precision and recall.Overall,the proposed approach highlights the effectiveness of interpretable,mathematically founded state estimation models as a reliable and efficient alternative to black-box deep learning systems in safety-critical UAV applications. 展开更多
关键词 Extended Kalman filter UAV tracking multi-object tracking mathematical modeling Bayesian estimation YOLOvl2 topological data analysis persistent homology
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Face-Pedestrian Joint Feature Modeling with Cross-Category Dynamic Matching for Occlusion-Robust Multi-Object Tracking 认领 引用
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作者 Qin Hu Hongshan Kong 《Computers, Materials & Continua》 SCIE EI 2026年第1期870-900,共31页
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
关键词 Cross-category dynamic binding joint feature modeling face-pedestrian association multi object tracking occlusion robustness
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D-DeepOCSORT: multi-object tracking algorithm based on LiDAR and monocular camera 认领 引用
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作者 Xiao HE Shuai REN +1 位作者 Chang LIU Lei SHI 《Optoelectronics Letters》 EI 2026年第2期118-123,共6页
To enhance the tracking stability of Deep OCSORT, this paper proposes a novel multi-sensor data fusion-based multi-object tracking(MOT) method. Specifically, we build upon the Deep OCSORT foundation and additionally i... To enhance the tracking stability of Deep OCSORT, this paper proposes a novel multi-sensor data fusion-based multi-object tracking(MOT) method. Specifically, we build upon the Deep OCSORT foundation and additionally integrate target velocity information directly measured by light detection and ranging(Li DAR). The introduction of this velocity information is conducted from three perspectives. Firstly, during data association, a penalty term is constructed based on the differences in target velocities to constrain generating matches with consistent velocities. Secondly, use Li DAR velocity for initialization and online updating of the velocity state within the tracker, making tracking predictions more stable. Thirdly, control the degree of dependence on velocity information by adjusting the process noise covariance matrix. Evaluation results on the KITTI dataset demonstrate that compared to the original Deep OCSORT, the proposed improved multi-source heterogeneous information fusion method significantly enhances tracking performance, with maximum improvements of 3.35, 3.26, and 3.71 on the higher order tracking accuracy(HOTA), multi-object tracking accuracy(MOTA), and interaction detection F1 score(IDF1) metrics, respectively. This study provides an effective approach to building a more stable and accurate MOT system. 展开更多
关键词 light detection ranging li dar enhance tracking stability penalty term velocity information integrate target velocity information deep ocsort foundation data association deep ocsort
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HAMOT:A Hierarchical Adaptive Framework for Robust Multi-Object Tracking in Complex Environments 认领 引用
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作者 Jahfar Khan Said Baz Peng Zhang +3 位作者 Mian Muhammad Kamal Heba G.Mohamed Muhammad Sheraz Teong Chee Chuah 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第10期947-969,共23页
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
关键词 Occlusions MOT low-resolution association trajectory tracking
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Cue-Tracker:Integrating Deep Appearance Features and Spatial Cues for Multi-Object Tracking 认领 引用
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作者 Sheeba Razzaq Majid Iqbal Khan 《Computers, Materials & Continua》 SCIE EI 2025年第12期5377-5398,共22页
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
关键词 Tracking by detection weak cues occlusion handling MOT challenge spatial features appearance features re-identification ID switches fusion
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An AIoT Monitoring System for Multi-Object Tracking and Alerting 认领 引用 被引量：5
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作者 Wonseok Jung Se-Han Kim +1 位作者 Seng-Phil Hong Jeongwook Seo 《Computers, Materials & Continua》 SCIE EI 2021年第4期337-348,共12页
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
关键词 Internet of Things multi-object tracking pig pen social network
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Multi-object tracking based on behaviour and partial observation 认领 引用 被引量：1
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作者 路红 费树岷 +1 位作者 郑建勇 张涛 《Journal of Southeast University(English Edition)》 EI CAS 2008年第4期468-472,共5页
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
关键词 multi-object tracking projection ratio occlusion ratio behaviour partial observation Kalman filter
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Online Multi-Object Tracking Under Moving Unmanned Aerial Vehicle Platform Based on Object Detection and Feature Extraction Network 认领 引用 被引量：2
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作者 刘增敏 王申涛 +1 位作者 姚莉秀 蔡云泽 《Journal of Shanghai Jiaotong university(Science)》 EI 2024年第3期388-399,共12页
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
关键词 moving unmanned aerial vehicle(UAV)platform small object feature extraction image registration multi-object tracking
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Multi-object tracking based on deep associated features for UAV applications 认领 引用 被引量：4
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作者 XIONG Lingyu TANG Guijin 《Optoelectronics Letters》 EI 2023年第2期105-111,共7页
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
关键词 Multi-object tracking deep associated features UAV applications
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Multi-Object Tracking Strategy of Autonomous Vehicle Using Modified Unscented Kalman Filter and Reference Point Switching 认领 引用 被引量：1
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作者 WANG Muyuan WU Xiaodong 《Journal of Shanghai Jiaotong university(Science)》 EI 2021年第5期607-614,共8页
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
关键词 multi-object tracking(MOT) light detection and ranging(LiDAR)sensor unscented Kalman filter(UKF) object occlusion
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LQTTrack:Multi-Object Tracking by Focusing on Low-Quality Targets Association 认领 引用
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作者 Suya Li Ying Cao +2 位作者 Hengyi Ren Dongsheng Zhu Xin Xie 《Computers, Materials & Continua》 SCIE EI 2024年第10期1449-1470,共22页
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
关键词 Low-quality targets association strategy feature fusion multi-object tracking tracking-by-detection
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Multi-Object Tracking Based on Segmentation and Collision Avoidance 认领 引用
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作者 Meng Zhao Junhui Wang +3 位作者 Maoyong Cao Peirui Bai Hongyan Gu Mingtao Pei 《Journal of Beijing Institute of Technology》 EI CAS 2018年第2期213-219,共7页
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
关键词 multi-object tracking conditional random field superpixel collision avoidance
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Preformer MOT:A transformer-based approach for multi-object tracking with global trajectory prediction 认领 引用 被引量：1
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作者 Yueying Wang Yuhao Qing +2 位作者 Kaer Huang Chuangyin Dang Zhengtian Wu 《Fundamental Research》 CSCD 2026年第1期423-431,共9页
Multi-Object Tracking(MOT)is designed to accurately ascertain the positions and trajectories of moving objects within a video sequence.While prevalent methodologies primarily link detected objects across successive fr... Multi-Object Tracking(MOT)is designed to accurately ascertain the positions and trajectories of moving objects within a video sequence.While prevalent methodologies primarily link detected objects across successive frames by leveraging appearance and motion attributes,some approaches incorporate implicit global correlations from multiple antecedent frames to delineate target trajectories.Nonetheless,the capability to predict trajectories over multiple future frames remains insufficiently explored,leading to a significant underutilization of pertinent information in MOT.To address this gap,we introduce a transformer-based methodology,termed Preformer MOT,which enhances the precision of nonlinear trajectory predictions in dynamic settings.This enhancement is achieved through an innovative combination of a novel motion estimation technique-trajectory prediction-and Kalman filtering.Our method not only utilizes historical trajectory data but also anticipates the future positions of the target objects up to n subsequent steps,thereby furnishing a comprehensive prediction of trajectories with extensive temporal correlations.Specifically,we develop a straightforward self-supervised trajectory prediction model that estimates the future positions of a target object based on previously observed positional data.During the correlation phase,if a trajectory disruption occurs due to overlapping,occlusion,or nonlinear movements of the detected objects,Preformer MOT is capable of making early predictions using data from multiple forthcoming frames to reestablish trajectory continuity.Empirical evaluations on pedestrian datasets such as DanceTrack and MOT17 demonstrate that our approach surpasses other contemporary state-of-the-art methods.Furthermore,Preformer MOT exhibits exceptional performance in complex marine environments,underscoring its adaptability and efficacy. 展开更多
关键词 Multi-object tracking Trajectory prediction Global association Non-linear motion Self-supervised learning
Multi-Object Tracking with Micro Aerial Vehicle 认领 引用 被引量：1
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作者 Yufeng Ji Weixing Li +2 位作者 Xiaolin Li Shikun Zhang Feng Pan 《Journal of Beijing Institute of Technology》 EI CAS 2019年第3期389-398,共10页
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
关键词 multi-object tracking salient detection kernelized correlation filter (KCF) micro aerial vehicle(MAV)
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Robust Multi-Object Fish Tracking in Dynamic Aquatic Environments via Attention-Enhanced YOLOv8 and LSTM-Based Trajectory Prediction 认领 引用
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作者 Feng-Cheng Lin Bo-Chiao Jan Hui-An Wu 《Computers, Materials & Continua》 SCIE EI 2026年第7期1577-1600,共24页
With the increasing refinement of ornamental fish culture,understanding fish behavioral patterns has become critical.Fish movements not only reflect daily activity ranges but also reveal responses to environmental cha... With the increasing refinement of ornamental fish culture,understanding fish behavioral patterns has become critical.Fish movements not only reflect daily activity ranges but also reveal responses to environmental changes such as water currents and obstacles.However,traditional manual observation is limited by manpower and time,making it difficult to record fish behaviors over long periods stably.Existing automated tracking techniques often suffer from ID switches and track interruptions caused by rapid fish movement,occlusions,or intermingling,which in turn degrade the reliability of subsequent analyses.This paper proposes a deep learning-based multi-object fish tracking system that integrates YOLOv8n for object detection and employs an IoU matching criterion to associate detections across consecutive frames,thereby maintaining object ID continuity.To further reduce ID loss under rapid motion and partial occlusion,a multiple-LSTM prediction model is introduced as a temporal compensation mechanism,thereby improving timing stability and track continuity.Moreover,considering disturbances in the experimental field(e.g.,water disturbance and water current interference)that can blur fish body edges and fine details,an attention-enhanced detector,YOLOv8-CS(Convolutional Block Attention Module and Squeeze-and-Excitation),is developed by embedding CBAM and SE modules into the YOLOv8 architecture to enhance detection accuracy in dynamic waters.Experimental results demonstrate that the proposed system effectively increases the Multiple Object Tracking Accuracy(MOTA)to 77.23%and significantly reduces ID switches to 42.5,ensuring more robust and continuous trajectory tracking compared to benchmark methods.This system provides a highly reliable tool for automated behavior analysis in complex and dynamic aquatic environments. 展开更多
关键词 LSTM trajectory prediction multi-object tracking YOLOv8-CS attention mechanism
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ORT:Occlusion-robust for multi-object tracking 认领 引用
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作者 Shoudong Han Hongwei Wang +1 位作者 En Yu Zhuo Hu 《Fundamental Research》 CSCD 2025年第3期1214-1220,共7页
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
关键词 Multi-object tracking Motion-based prediction Trajectory reconnection Data association Long-term occlusion modeling
Online Multi-Object Tracking Based on Record Confidence and Hierarchical Association for Cyber-Physical Social Intelligence 认领 引用
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作者 Jieming Yang Dezhen Feng +1 位作者 Yuan Gao Cong Liu 《Big Data Mining and Analytics》 EI CSCD 2025年第4期851-866,共16页
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
关键词 multi-object tracking deep learning object detection neural network
A self-learning refined model and tracking for near space hypersonic vehicle by space-based radar 认领 引用 被引量：1
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作者 Yue XU Quan PAN +2 位作者 Zengfu WANG Hua LAN Shuling JIN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第5期504-529,共26页
The Near Space Hypersonic Vehicle(NSHV)features a unique design and propulsion system,achieving exceptional speed,range,and maneuverability,which challenge ground-based radars.Space-Based Radar(SBR)offers a breakthrou... The Near Space Hypersonic Vehicle(NSHV)features a unique design and propulsion system,achieving exceptional speed,range,and maneuverability,which challenge ground-based radars.Space-Based Radar(SBR)offers a breakthrough for tracking NSHV targets,with allweather operation and freedom from Earth's curvature,but faces complex coordinate transformations.Traditional models often overlook the NSHV's dynamic gliding trajectory,especially the impact of hidden control variables on maneuvering,causing mismatches during rapid motion changes.This paper proposes a refined tracking model unified in the ECEF coordinate frame,incorporating model parameters that implicitly encode control laws,and presents an ExpectationMaximization Multi-swarm Cooperative Particle Swarm Optimization(EM-MCPSO)framework for both NSHV tracking and model parameter estimation to address this problem.To minimize conversion errors,a transformation matrix directly represented by the state in the EarthCentered Earth-Fixed(ECEF)coordinate is derived.Then the hybrid aerodynamic acceleration coefficients are introduced to precisely describe the dynamic behaviors,formulating target tracking as a joint estimation problem of state and parameters within EM framework.Finally,a self-learning algorithm based on a master–slave structured PSO is proposed to solve the optimization of the conditional expectations of EM under strong nonlinearity,with a Proportional-Derivative(PD)controller accelerating convergence,and updating the population structure with historical data.Simulations of vertical gliding and horizontal maneuvers validate the algorithm's effectiveness. 展开更多
关键词 Dynamics modeling Expectation Maximization(EM) Maneuvering target tracking Near Space Hypersonic Vehicle(NSHV) Particle Swarm Optimization(PSO)
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