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8 May 2022 Research on vehicle tracking method based on UAV video
Yipen Wu, Ying Wang, Dun Zhang, Zhikai Huang, Zhijian Gu, Bo Wang
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Proceedings Volume 12249, 2nd International Conference on Internet of Things and Smart City (IoTSC 2022); 1224939 (2022) https://doi.org/10.1117/12.2636626
Event: 2022 2nd International Conference on Internet of Things and Smart City (IoTSC 2022), 2022, Xiamen, China
Abstract
In the road video shooting of unmanned aerial vehicle (UAV), the UAV is in an unconstrained state, and the video picture is jitter and unstable. When tracking multiple vehicles in uav video, identity switching of tracking targets is easy to occur. Based on this, an improved DeepSort vehicle target tracking algorithm based on Yolov5s is proposed. When Yolov5s trains the vehicle detection model, DIOU-NMS is used instead of NMS to remove the redundant target position prediction box. Aiming at the fact that DeepSort pre-trained appearance extraction model did not contain the appearance model of the vehicle, the vehicle appearance model was obtained by using the lightweight ShuffleNet V2 network for vehicle rerecognition training on VeRi data. The improved algorithm is used to evaluate the MOT of UAVDT data sets. The experimental results show that compared with the original algorithm, the tracking identity switching (IDSW) of this experimental method is reduced by 49.1%, MOTA is improved by 3.2% and MOTP is improved by 0.3%.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yipen Wu, Ying Wang, Dun Zhang, Zhikai Huang, Zhijian Gu, and Bo Wang "Research on vehicle tracking method based on UAV video", Proc. SPIE 12249, 2nd International Conference on Internet of Things and Smart City (IoTSC 2022), 1224939 (8 May 2022); https://doi.org/10.1117/12.2636626
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KEYWORDS
Video

Target detection

Detection and tracking algorithms

Data modeling

Unmanned aerial vehicles

Performance modeling

Sensors

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