计算机科学
人工智能
RGB颜色模型
过程(计算)
帧(网络)
轮廓
计算机视觉
任务(项目管理)
深度学习
高级驾驶员辅助系统
工程类
电信
操作系统
系统工程
作者
Imen Jegham,Anouar Ben Khalifa,Ihsen Alouani,Mohamed Ali Mahjoub
标识
DOI:10.1109/jsen.2020.3019258
摘要
Driver behaviors and decisions are crucial factors for on-road driving safety. With a precise driver behavior monitoring system, traffic accidents and injuries can be significantly reduced. However, understanding human behaviors in real-world driving settings is a challenging task because of the uncontrolled conditions including illumination variation, occlusion, and dynamic and cluttered background. In this paper, a Kinect sensor, which provides multimodal signals, is adopted as a driver monitoring sensor to recognize safe driving and common secondary most distracting in-vehicle actions. We propose a novel soft spatial attention-based network named the Depth-based Spatial Attention network (DSA), which adds a cognitive process to deep network by selectively focusing on the driver's silhouette and motion in the cluttered driving scene. In fact, at each time t, we introduce a new weighted RGB frame based on an attention model designed using a depth frame. The final classification accuracy is substantially enhanced compared to the state-of-the-art results with an achieved improvement of up to 27%.
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