Video processing is one challenge in collecting vehicle trajectories from unmanned aerial vehicle(UAV) and road boundary estimation is one way to improve the video processing algorithms. However, current methods do no...Video processing is one challenge in collecting vehicle trajectories from unmanned aerial vehicle(UAV) and road boundary estimation is one way to improve the video processing algorithms. However, current methods do not work well for low volume road, which is not well-marked and with noises such as vehicle tracks. A fusion-based method termed Dempster-Shafer-based road detection(DSRD) is proposed to address this issue. This method detects road boundary by combining multiple information sources using Dempster-Shafer theory(DST). In order to test the performance of the proposed method, two field experiments were conducted, one of which was on a highway partially covered by snow and another was on a dense traffic highway. The results show that DSRD is robust and accurate, whose detection rates are 100% and 99.8% compared with manual detection results. Then, DSRD is adopted to improve UAV video processing algorithm, and the vehicle detection and tracking rate are improved by 2.7% and 5.5%,respectively. Also, the computation time has decreased by 5% and 8.3% for two experiments, respectively.展开更多
Unmanned aerial vehicle(UAV)tracking is a critical task in surveillance,security,and autonomous navigation applications.In this study,we propose graph neural network-tracker(GNN-tracker),a novel GNN-based UAV tracking...Unmanned aerial vehicle(UAV)tracking is a critical task in surveillance,security,and autonomous navigation applications.In this study,we propose graph neural network-tracker(GNN-tracker),a novel GNN-based UAV tracking framework that effectively integrates graph-based spatial-temporal modelling,Transformer-based feature extraction,and multi-sensor fusion to enhance tracking robustness and accuracy.Unlike traditional tracking approaches,GNNtracker dynamically constructs a spatiotemporal graph representation,improving identity consistency and reducing tracking errors under OCC-heavy scenarios.Experimental evaluations on optical,thermal,and fused UAV datasets demonstrate the superiority of GNN-tracker(fused)over state-of-the-art methods.The proposed model achieves multiple object tracking accuracy(MOTA)scores of 91.4%(fused),89.1%(optical),and 86.3%(thermal),surpassing TransT by 8.9%in MOTA and 7.7%in higher order tracking accuracy(HOTA).The HOTA scores of 82.3%(fused),80.1%(optical),and 78.7%(thermal)validate its strong object association capabilities,while its frames per second of 58.9(fused),56.8(optical),and 54.3(thermal)ensures real-time performance.Additionally,ablation studies confirm the essential role of graph-based modelling and multi-sensor fusion,with performance drops of up to 8.9%in MOTA when these components are removed.Thus,GNN-tracker(fused)offers a highly accurate,robust,and efficient UAV tracking solution,effectively addressing real-world challenges across diverse environmental conditions and multiple sensor modalities.展开更多
When tracking a unmanned aerial vehicle(UAV)in complex backgrounds,environmen-tal noise and clutter often obscure it.Traditional radar target tracking algorithms face multiple lim-itations when tracking a UAV,includin...When tracking a unmanned aerial vehicle(UAV)in complex backgrounds,environmen-tal noise and clutter often obscure it.Traditional radar target tracking algorithms face multiple lim-itations when tracking a UAV,including high vulnerability to target occlusion and shape variations,as well as pronounced false alarms and missed detections in low signal-to-noise ratio(SNR)envi-ronments.To address these issues,this paper proposes a UAV detection and tracking algorithm based on a low-frequency communication network.The accuracy and effectiveness of the algorithm are validated through simulation experiments using field-measured point cloud data.Additionally,the key parameters of the algorithm are optimized through a process of selection and comparison,thereby improving the algorithm's precision.The experimental results show that the improved algo-rithm can significantly enhance the detection and tracking performance of the UAV under high clutter density conditions,effectively reduce the false alarm rate and markedly improve overall tracking performance metrics.展开更多
基金Project(2009AA11Z220)supported by the National High Technology Research and Development Program of China
文摘Video processing is one challenge in collecting vehicle trajectories from unmanned aerial vehicle(UAV) and road boundary estimation is one way to improve the video processing algorithms. However, current methods do not work well for low volume road, which is not well-marked and with noises such as vehicle tracks. A fusion-based method termed Dempster-Shafer-based road detection(DSRD) is proposed to address this issue. This method detects road boundary by combining multiple information sources using Dempster-Shafer theory(DST). In order to test the performance of the proposed method, two field experiments were conducted, one of which was on a highway partially covered by snow and another was on a dense traffic highway. The results show that DSRD is robust and accurate, whose detection rates are 100% and 99.8% compared with manual detection results. Then, DSRD is adopted to improve UAV video processing algorithm, and the vehicle detection and tracking rate are improved by 2.7% and 5.5%,respectively. Also, the computation time has decreased by 5% and 8.3% for two experiments, respectively.
文摘Unmanned aerial vehicle(UAV)tracking is a critical task in surveillance,security,and autonomous navigation applications.In this study,we propose graph neural network-tracker(GNN-tracker),a novel GNN-based UAV tracking framework that effectively integrates graph-based spatial-temporal modelling,Transformer-based feature extraction,and multi-sensor fusion to enhance tracking robustness and accuracy.Unlike traditional tracking approaches,GNNtracker dynamically constructs a spatiotemporal graph representation,improving identity consistency and reducing tracking errors under OCC-heavy scenarios.Experimental evaluations on optical,thermal,and fused UAV datasets demonstrate the superiority of GNN-tracker(fused)over state-of-the-art methods.The proposed model achieves multiple object tracking accuracy(MOTA)scores of 91.4%(fused),89.1%(optical),and 86.3%(thermal),surpassing TransT by 8.9%in MOTA and 7.7%in higher order tracking accuracy(HOTA).The HOTA scores of 82.3%(fused),80.1%(optical),and 78.7%(thermal)validate its strong object association capabilities,while its frames per second of 58.9(fused),56.8(optical),and 54.3(thermal)ensures real-time performance.Additionally,ablation studies confirm the essential role of graph-based modelling and multi-sensor fusion,with performance drops of up to 8.9%in MOTA when these components are removed.Thus,GNN-tracker(fused)offers a highly accurate,robust,and efficient UAV tracking solution,effectively addressing real-world challenges across diverse environmental conditions and multiple sensor modalities.
基金supported in part by National Natural Science Founda-tion of China(No.62372284)in part by Shanghai Nat-ural Science Foundation(No.24ZR1421800).
文摘When tracking a unmanned aerial vehicle(UAV)in complex backgrounds,environmen-tal noise and clutter often obscure it.Traditional radar target tracking algorithms face multiple lim-itations when tracking a UAV,including high vulnerability to target occlusion and shape variations,as well as pronounced false alarms and missed detections in low signal-to-noise ratio(SNR)envi-ronments.To address these issues,this paper proposes a UAV detection and tracking algorithm based on a low-frequency communication network.The accuracy and effectiveness of the algorithm are validated through simulation experiments using field-measured point cloud data.Additionally,the key parameters of the algorithm are optimized through a process of selection and comparison,thereby improving the algorithm's precision.The experimental results show that the improved algo-rithm can significantly enhance the detection and tracking performance of the UAV under high clutter density conditions,effectively reduce the false alarm rate and markedly improve overall tracking performance metrics.
