Pose-guided multi-task video transformer for driver action recognition

• URL: http://arxiv.org/abs/2407.13750v1
• Date: Thu, 18 Jul 2024 17:53:51 GMT
• Title: Pose-guided multi-task video transformer for driver action recognition
• Authors: Ricardo Pizarro, Roberto Valle, Luis Miguel Bergasa, José M. Buenaposada, Luis Baumela,
• Abstract summary: We introduce a multi-task video transformer that predicts both distracted actions and driver pose. Our model surpasses existing state-of-the art results in driver action recognition while exhibiting superior efficiency compared to current video transformer-based approaches.
• Score: 5.392333248149453
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We investigate the task of identifying situations of distracted driving through analysis of in-car videos. To tackle this challenge we introduce a multi-task video transformer that predicts both distracted actions and driver pose. Leveraging VideoMAEv2, a large pre-trained architecture, our approach incorporates semantic information from human keypoint locations to enhance action recognition and decrease computational overhead by minimizing the number of spatio-temporal tokens. By guiding token selection with pose and class information, we notably reduce the model's computational requirements while preserving the baseline accuracy. Our model surpasses existing state-of-the art results in driver action recognition while exhibiting superior efficiency compared to current video transformer-based approaches.

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