| Citation: | YANG Bin, LI Heping, ZENG Huiet al. Three-dimensional human pose estimation based on video[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(12): 2463-2469. doi: 10.13700/j.bh.1001-5965.2019.0384(in Chinese)
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The existing 3D human pose estimation method focuses on estimating the 3D pose of the human body through a single frame image, while ignoring the correlation between the front and back frames in the video. Therefore, by investigating the information of the video in the time dimension, the accuracy of the 3D human pose estimation can be further improved. Based on this, the convolutional neural network structure that can fully extract the temporal information in the video is designed. It has the advantage of low computational resources and high precision. The complete 3D human pose can be restored only by using the coordinates of the 2D articulation point as input. Furthermore, a new loss function is proposed, which uses the continuity of human pose between adjacent frames to improve the smoothness of 3D pose estimation in video sequences, and also solves the problem of accuracy degradation due to lack of inter-frame information. By testing on the Human 3.6M dataset, the experimental results indicate that the average test error of the proposed method is 1.2 mm lower than that of the current standard 3D pose estimation algorithm, and the proposed method has a high accuracy for the 3D human pose estimation of video sequences.
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