Pedestrian Angle Recognition Based on JDE Multi-object Tracking Algorithm

We often execute multi-object tracking algorithms and pedestrian angle recognition algorithms independently when we solve complex problems that need to track multiple pedestrians and identify their angles simultaneously. Firstly, the multi-target tracking algorithm is used to locate the target and d...

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Vydáno v:	2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP) s. 647 - 651
Hlavní autoři:	Yang, Chunde, Xu, Tongyao, Lv, Minghong, Yu, Yi, Jiang, Xiaoming
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 15.04.2022
Témata:	Feature extraction JDE multi-object tracking algorithm pedestrian angle recognition real-time Signal processing Signal processing algorithms Streaming media Surveillance surveillance scene Target recognition Target tracking
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Popis
Shrnutí:	We often execute multi-object tracking algorithms and pedestrian angle recognition algorithms independently when we solve complex problems that need to track multiple pedestrians and identify their angles simultaneously. Firstly, the multi-target tracking algorithm is used to locate the target and determine the target's identity. Then the pedestrian angle recognition model is used to recognize the pedestrian angle. This strategy is complicated and inefficient since the two models repeatedly extract target features. This paper realizes the combination of multi-object tracking algorithm and pedestrian angle recognition algorithm in response to this problem. Specifically, we added a new classification branch based on the JDE multi-object tracking algorithm. It enables the model to perform pedestrian angle recognition while detecting and embedding feature extraction. Finally, experiments show that the improved algorithm can perform pedestrian tracking and pedestrian angle recognition simultaneously. The algorithm's ability to track large and medium targets is the same as the JDE multi-object tracking algorithm. Compared with the Hydraplus model, the improved algorithm has a higher recognition accuracy. Besides, the algorithm can achieve real-time performance.
DOI:	10.1109/ICSP54964.2022.9778590