Multiple Scale Faster-RCNN Approach to Driver’s Cell-Phone Usage and Hands on Steering Wheel Detection

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Abstract

In this paper, we present an advanced deep learning based approach to automatically determine whether a driver is using a cell-phone as well as detect if his/her hands are on the steering wheel (i.e. counting the number of hands on the wheel). To robustly detect small objects such as hands, we propose Multiple Scale Faster-RCNN (MSFRCNN) approach that uses a standard Region Proposal Network (RPN) generation and incorporates feature maps from shallower convolution feature maps, i.e. conv3 and conv4, for ROI pooling. In our driver distraction detection framework, we first make use of the proposed MS-FRCNN to detect individual objects, namely, a hand, a cell-phone, and a steering wheel. Then, the geometric information is extracted to determine if a cell-phone is being used or how many hands are on the wheel. The proposed approach is demonstrated and evaluated on the Vision for Intelligent Vehicles and Applications (VIVA) Challenge database and the challenging Strategic Highway Research Program (SHRP-2) face view videos that was acquired to monitor drivers under naturalistic driving conditions. The experimental results show that our method archives better performance than Faster R-CNN on both hands on wheel detection and cell-phone usage detection while remaining at similar testing cost. Compare to the state-of-the-art cell-phone usage detection, our approach obtains higher accuracy, is less time consuming and is independent to landmarking. The groundtruth database will be publicly available.

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