Transfer-LMR: Heavy-Tail Driving Behavior Recognition in Diverse Traffic Scenarios
<|reference_start|>Transfer-LMR: Heavy-Tail Driving Behavior Recognition in Diverse Traffic Scenarios: 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.<|reference_end|>
@article{parikh2024transfer-lmr:,
    title={Transfer-LMR: Heavy-Tail Driving Behavior Recognition in Diverse Traffic
  Scenarios},
    author={Chirag Parikh, Ravi Shankar Mishra, Rohan Chandra, Ravi Kiran
  Sarvadevabhatla},
    journal={arXiv preprint arXiv:2405.05354},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.05354},
    primaryClass={cs.CV}
}