  Distracted driving causes thousands of deaths per year, and how to apply
deep-learning methods to prevent these tragedies has become a crucial problem.
In Track3 of the 6th AI City Challenge, researchers provide a high-quality
video dataset with densely action annotations. Due to the small data scale and
unclear action boundary, the dataset presents a unique challenge to precisely
localize all the different actions and classify their categories. In this
paper, we make good use of the multi-view synchronization among videos, and
conduct robust Multi-View Practice (MVP) for driving action localization. To
avoid overfitting, we fine-tune SlowFast with Kinetics-700 pre-training as the
feature extractor. Then the features of different views are passed to
ActionFormer to generate candidate action proposals. For precisely localizing
all the actions, we design elaborate post-processing, including model voting,
threshold filtering and duplication removal. The results show that our MVP is
robust for driving action localization, which achieves 28.49% F1-score in the
Track3 test set.
