  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.
