A Driver’s Calling Behavior Detection Method Based on Deep Learning - XIONG Qunfang; LIN Jun; YUE Wei; LIU Shiwang; LUO Xiao; DING Chi (Kongzhi Yu Xinxi Jishu)
In order to prevent the driver from being distracted by the cell phone call, real-time monitoring of drivers’ behavior through video analysis is especially important. At present, driver’s calling behavior detection methods are prone to object occlusion, image rotation, illumination change and are difficult to extract deep features of the image, which degrade the detection accuracy. This paper proposed a driver’s cell phone calling behavior detection algorithm based on deep learning. The algorithm comprises two steps. Firstly, face detection and face tracking is supported by PCN (progressive calibration networks) to determine the calling detection area. Secondly, the driver’s cell phone calling behavior detection method based on convolutional neural network is used to detect the cell phone in the candidate area. Experimental test results show that the proposed algorithm has high robutness, and its accuracy reaches 96.56%, the false positive rate reaches 1.52%, and the processing speed reaches 25 frames per second. It can effectively detect the driver’s calling behavion.