Hazardous action recognition system based on blazepose and ST-recurrent neural network

Zhengyi Ma; Hao Zhang; Yingshuo Feng; Chenyang Yang; Jiayin Zhu; Yaming Niu

doi:10.1117/12.2671503

16 March 2023 Hazardous action recognition system based on blazepose and ST-recurrent neural network

Zhengyi Ma, Hao Zhang, Yingshuo Feng, Chenyang Yang, Jiayin Zhu, Yaming Niu

Proceedings Volume 12593, Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022); 1259311 (2023) https://doi.org/10.1117/12.2671503
Event: 2nd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022), 2022, Guangzhou, China

Abstract

This paper focuses on the recognition and classification of driver's dangerous driving actions through Blazepose algorithm and st-gru network to ensure that drivers can drive safely during the driving process and keep drivers safe at all times. blazepose is a lightweight human posture estimation model using blazepsoe method to replace the openpose method in human skeletal keypoints to improve the speed and reduce the model size. The st-gru network is one of the best action recognition models based on human skeletal keypoints, which is better than most of the current action recognition models in terms of model size, accuracy and recall value. Therefore, this project uses the st-gru network to classify the extracted human skeletal keypoint.

Citation Download Citation

Zhengyi Ma, Hao Zhang, Yingshuo Feng, Chenyang Yang, Jiayin Zhu, and Yaming Niu "Hazardous action recognition system based on blazepose and ST-recurrent neural network", Proc. SPIE 12593, Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022), 1259311 (16 March 2023); https://doi.org/10.1117/12.2671503

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KEYWORDS

Action recognition

RGB color model

Pose estimation

Detection and tracking algorithms

Performance modeling

Autonomous driving

Data modeling

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