Rethinking Top Probability from Multi-view for Distracted Driver Behaviour Localization

TL;DR

This work adopts an action recognition model based on self-supervise learning to detect distracted activities and give potential action probabilities, and introduces a conditional post-processing operation to locate distracted behaviours and action temporal boundaries precisely.

Abstract

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.

Figures (6)

• Figure 1: Distracted Driver Behaviour Recognition System
• Figure 2: Ensemble strategy
• Figure 3: Conditional Merging
• Figure 4: Conditional Decision
• Figure 5: Missing Labels Restoring