MIFI: MultI-camera Feature Integration for Roust 3D Distracted Driver Activity Recognition
This work addresses distracted driver recognition for intelligent transportation systems, but it is incremental as it builds on existing multi-view and difficulty-aware methods.
The authors tackled 3D distracted driver activity recognition by proposing MIFI, a multi-camera feature integration approach with example re-weighting, which improved performance on the 3MDAD dataset compared to single-view models.
Distracted driver activity recognition plays a critical role in risk aversion-particularly beneficial in intelligent transportation systems. However, most existing methods make use of only the video from a single view and the difficulty-inconsistent issue is neglected. Different from them, in this work, we propose a novel MultI-camera Feature Integration (MIFI) approach for 3D distracted driver activity recognition by jointly modeling the data from different camera views and explicitly re-weighting examples based on their degree of difficulty. Our contributions are two-fold: (1) We propose a simple but effective multi-camera feature integration framework and provide three types of feature fusion techniques. (2) To address the difficulty-inconsistent problem in distracted driver activity recognition, a periodic learning method, named example re-weighting that can jointly learn the easy and hard samples, is presented. The experimental results on the 3MDAD dataset demonstrate that the proposed MIFI can consistently boost performance compared to single-view models.