MIFI: MultI-Camera Feature Integration for Robust 3D Distracted Driver Activity Recognition

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 w...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2024-01, Vol.25 (1), p.338-348
Main Authors: Kuang, Jian, Li, Wenjing, Li, Fang, Zhang, Jun, Wu, Zhongcheng
Format: Article
Language:English
Subjects:
Activity recognition
Behavioral sciences
Cameras
Distracted driver recognition
Driver behavior
example re-weighting
Feature extraction
Intelligent transportation systems
multi-view feature learning
Risk management
Road accidents
Road safety
Three-dimensional displays
Vehicles
Weighting
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Summary: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.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2023.3304317