Driver pose estimation based on dual-stream fully convolutional network

To solve the problems of non-target misdetection and low accuracy under the complex environmental conditions in the cab by the existing pose estimation method, a driver pose estimation method was proposed based on dual-stream fully convolutional network（FCN）. Two independent FCN branches were established to predict the coordinates of keypoints and the connection information between keypoints, and the hourglass network structure was set in the two branches to enhance the ability of network for extracting key information. In order to further improve the feature extraction capability of the network, the feature maps obtained from the shallow and deep networks were fused. Common objects in context（COCO）data set and driver′s driving situation（DDS）data set were used to verify the detection effect of the proposed method. The experimental results show that the detection accuracies of the proposed method in COCO data set and DDS data set are respective 64.5% and 78.4%, which illuminates that the proposed method is superior to other three comparison algorithms. The proposed method can improve the detection accuracy of the driver posture with good robustness.