基于JDE多目标跟踪算法的行人角度识别

C. Yang, Tongyao Xu, Minghong Lv, Yi Yu, Xiaoming Jiang
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引用次数: 0

摘要

当我们解决需要同时跟踪多个行人并识别其角度的复杂问题时,我们经常独立执行多目标跟踪算法和行人角度识别算法。首先,利用多目标跟踪算法对目标进行定位,确定目标的身份;然后利用行人角度识别模型对行人角度进行识别。由于两种模型重复提取目标特征,该策略复杂且效率低下。针对这一问题,本文实现了多目标跟踪算法与行人角度识别算法的结合。具体来说,我们增加了一个新的基于JDE多目标跟踪算法的分类分支。它使模型能够在检测和嵌入特征提取的同时进行行人角度识别。实验结果表明,改进后的算法可以同时进行行人跟踪和行人角度识别。该算法跟踪大中型目标的能力与JDE多目标跟踪算法相同。与Hydraplus模型相比,改进算法具有更高的识别精度。此外,该算法还能实现实时性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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.
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