Transfer-LMR: Heavy-Tail Driving Behavior Recognition in Diverse Traffic Scenarios
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916239456075776 |
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| author | Parikh, Chirag Mishra, Ravi Shankar Chandra, Rohan Sarvadevabhatla, Ravi Kiran |
| author_facet | Parikh, Chirag Mishra, Ravi Shankar Chandra, Rohan Sarvadevabhatla, Ravi Kiran |
| contents | Recognizing driving behaviors is important for downstream tasks such as reasoning, planning, and navigation. Existing video recognition approaches work well for common behaviors (e.g. "drive straight", "brake", "turn left/right"). However, the performance is sub-par for underrepresented/rare behaviors typically found in tail of the behavior class distribution. To address this shortcoming, we propose Transfer-LMR, a modular training routine for improving the recognition performance across all driving behavior classes. We extensively evaluate our approach on METEOR and HDD datasets that contain rich yet heavy-tailed distribution of driving behaviors and span diverse traffic scenarios. The experimental results demonstrate the efficacy of our approach, especially for recognizing underrepresented/rare driving behaviors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05354 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Transfer-LMR: Heavy-Tail Driving Behavior Recognition in Diverse Traffic Scenarios Parikh, Chirag Mishra, Ravi Shankar Chandra, Rohan Sarvadevabhatla, Ravi Kiran Computer Vision and Pattern Recognition Recognizing driving behaviors is important for downstream tasks such as reasoning, planning, and navigation. Existing video recognition approaches work well for common behaviors (e.g. "drive straight", "brake", "turn left/right"). However, the performance is sub-par for underrepresented/rare behaviors typically found in tail of the behavior class distribution. To address this shortcoming, we propose Transfer-LMR, a modular training routine for improving the recognition performance across all driving behavior classes. We extensively evaluate our approach on METEOR and HDD datasets that contain rich yet heavy-tailed distribution of driving behaviors and span diverse traffic scenarios. The experimental results demonstrate the efficacy of our approach, especially for recognizing underrepresented/rare driving behaviors. |
| title | Transfer-LMR: Heavy-Tail Driving Behavior Recognition in Diverse Traffic Scenarios |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.05354 |