Summary of Rethinking Top Probability From Multi-view For Distracted Driver Behaviour Localization, by Quang Vinh Nguyen et al.
Rethinking Top Probability from Multi-view for Distracted Driver Behaviour Localization
by Quang Vinh Nguyen, Vo Hoang Thanh Son, Chau Truong Vinh Hoang, Duc Duy Nguyen, Nhat Huy Nguyen Minh, Soo-Hyung Kim
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the naturalistic driving action localization task, recognizing and understanding human behaviors from real-world driving scenarios. The authors improve upon previous approaches by introducing a self-supervised learning-based action recognition model that detects distracted activities and provides potential probabilities. A constraint ensemble strategy leveraging multi-camera views enhances robustness, while a conditional post-processing operation refines the detection of distracted behaviors and temporal boundaries. The method achieves the sixth position on the public leaderboard for Track 3 of the 2024 AI City Challenge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to recognize human behaviors in real-world driving scenarios. It uses self-supervised learning to detect distracted activities and provide potential probabilities. Then, it combines multiple camera views to get more accurate results. Finally, it refines the detection by looking at when these actions happen. The method does well on a public leaderboard. |
Keywords
» Artificial intelligence » Self supervised
