Dangerous driving pose recognition based on MobileNetV3 and ST-SRU

Abstract

In the face of frequent traffic accidents caused by dangerous driving behaviors, this paper proposes a dangerous driving pose recognition system based on MobileNetV3 and ST-SRU. Firstly, the network structure of MobileNetV3 is modified to be used for human pose estimation, and the heatmaps and offsets of joint points are output to estimate the 2D coordinate positions of J joint points. Then, the ST-SRU skeleton action recognition algorithm is defined, and the actions are classified by using skeleton sequence data. The experimental results show that the PCP (percentage correct parts) of MobileNetV3 pose estimation algorithm is 95.6 % on the self-built AI Challenger upper limb attitude dataset, and the time of 1 000 tests is only 5.03 seconds. By using the self-built dangerous driving behavior dataset, the trained pose estimation and action recognition model is transplanted to the embedded platform, and the real-time dangerous driving pose recognition system is realized.