Rethinking Top Probability from Multi-view for Distracted Driver Behaviour Localization

• URL: http://arxiv.org/abs/2411.12525v1
• Date: Tue, 19 Nov 2024 14:18:02 GMT
• Title: Rethinking Top Probability from Multi-view for Distracted Driver Behaviour Localization
• Authors: Quang Vinh Nguyen, Vo Hoang Thanh Son, Chau Truong Vinh Hoang, Duc Duy Nguyen, Nhat Huy Nguyen Minh, Soo-Hyung Kim,
• Abstract summary: Action localization task aims to recognize and comprehend human behaviors and actions from video data captured during real-world driving scenarios. Previous studies have shown great action localization performance by applying a recognition model followed by probability-based post-processing. In this work, we adopt an action recognition model based on self-supervise learning to detect distracted activities and give potential action probabilities.
• Score: 6.531367337657802
• License:
• Abstract: Naturalistic driving action localization task aims to recognize and comprehend human behaviors and actions from video data captured during real-world driving scenarios. Previous studies have shown great action localization performance by applying a recognition model followed by probability-based post-processing. Nevertheless, the probabilities provided by the recognition model frequently contain confused information causing challenge for post-processing. In this work, we adopt an action recognition model based on self-supervise learning to detect distracted activities and give potential action probabilities. Subsequently, a constraint ensemble strategy takes advantages of multi-camera views to provide robust predictions. Finally, we introduce a conditional post-processing operation to locate distracted behaviours and action temporal boundaries precisely. Experimenting on test set A2, our method obtains the sixth position on the public leaderboard of track 3 of the 2024 AI City Challenge.

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