Rethinking Top Probability from Multi-view for Distracted Driver Behaviour Localization
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913581146046464 |
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| author | Nguyen, Quang Vinh Son, Vo Hoang Thanh Hoang, Chau Truong Vinh Nguyen, Duc Duy Minh, Nhat Huy Nguyen Kim, Soo-Hyung |
| author_facet | Nguyen, Quang Vinh Son, Vo Hoang Thanh Hoang, Chau Truong Vinh Nguyen, Duc Duy Minh, Nhat Huy Nguyen Kim, Soo-Hyung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_12525 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Rethinking Top Probability from Multi-view for Distracted Driver Behaviour Localization Nguyen, Quang Vinh Son, Vo Hoang Thanh Hoang, Chau Truong Vinh Nguyen, Duc Duy Minh, Nhat Huy Nguyen Kim, Soo-Hyung Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Rethinking Top Probability from Multi-view for Distracted Driver Behaviour Localization |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2411.12525 |