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Real-time Hands-Motion Tracking of Car Driver over Steering-Wheel by Combination of Particle Filter with Deep Neural Network for Body-Region Extraction

Recent advances of image segmentation techniques based on deep neural network provide us accurate body region extraction from a camera-captured image in real-time processing with the aid of parallel computing environment of GPU. Enough accuracy of the extracted body region over the image can be utilized to various applications including car-driver's hands-motion tracking over steering-wheel. Conventional researches of the hands-motion tracking have employed color cue in ordinary camera, which is strongly affected by lighting conditions, and/or depth information, in which a special depth sensor is necessary to use and post-processing over the depth image to extract a body region is required. We have investigated in this paper to incorporate the deep neural network based human body segmentation into particle filter based hands-motion tracking as a newly proposed method. In this method, extracted body region including hands and arms can be evaluated by extended likelihood to the arm direction in the particle filter algorithm. Real-time implementation of the proposed method to a driving simulator facility in the laboratory has been reported with some experimental results.