Human Pose Estimation Algorithm Based on Channel Splitting

Abstract

Abstract: To improve the accuracy and speed of human pose estimation, a channel-split-based human pose estimation algorithm, named Channel-Split Residual Steps Network (Channel-Split RSN), is proposed. First of all, channel-split blocks are proposed to apply convolution operation for split feature in order to obtain rich feature representation. Then, feature enhancement blocks are introduced to further split feature channel and employ different strategies for different groups which can reduce similar features in feature channels. Finally, to further enhance the pose refine machine in Channel-Split RSN, combined with improved spatial attention mechanism, a pose refine machine based on feature spatial correlation, named Context-PRM, is proposed. Experimental results show that on the COCO test-dev dataset, our algorithm reaches 75.9% AP and 55.36 FPS, and the Params(M) of the model is only 18.3. Compared with the traditional RSN18 and RSN50, the AP of the model is improved by 5 and 3.4 percentage points, respectively. FPS is 12.08 faster than the traditional RSN50. On the more challenging CrowdPose dataset, our approach achieves 66.9% AP and 19.16 FPS, an AP improvement of 4.6 percentage points compared to RSN18, which effectively improves the accuracy of human pose estimation and the model has a faster recognition speed. Our source code is available at https://github.com/qdd1234/Channel-Split-RSN.

Key words: Channel-Split RSN, human pose estimation, channel-split block, feature enhancement block, Context-PRM

ZHOU Kun-yang, ZHAO Meng-ting, ZHANG Hai-chao, SHAO Ye-qin. Human Pose Estimation Algorithm Based on Channel Splitting[J]. Computer and Modernization, 2021, 0(12): 27-36.

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