Appearance-posture fusion network for distracted driving behavior recognition

Author:

Yang XiaohuiORCID,Qiao Yu,Han Shiyuan,Feng Zhen,Chen Yuehui

Publisher

Elsevier BV

Reference46 articles.

1. Real-time driver distraction recognition: A hybrid genetic deep network based approach;Aljohani;Alexandria Engineering Journal,2023

2. Aggregating CNN and HOG features for real-time distracted driver detection;Arefin,2019

3. Baradel, F., Wolf, C., Mille, J., & Taylor, G. W. (2018). Glimpse clouds: Human activity recognition from unstructured feature points. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 469–478).

4. Context-driven Multi-stream LSTM (M-LSTM) for recognizing fine-grained activity of drivers;Behera,2018

5. Bertasius, G., Wang, H., & Torresani, L. (2021). Is space-time attention all you need for video understanding. In Proceedings of the international conference on machine learning (pp. 4–5).

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