A Lightweight Top-Down Multi-Person Pose Estimation Method Based on Symmetric Transformation and Global Matching

Abstract

The top-down human pose estimation method usually faces the following problems: (i) The target detection result is not well applied in the pose estimation network. (ii) Difficulty of human detection in the crowded state. (iii) The complicated model leads to a long training time. Aiming at the issues above, a lightweight multi-person pose estimation method based on symmetric transformation and global matching is proposed. Symmetric transformation module adds spatial transformation network(STN) and spatial de-transformer network(SDTN) before and after the single-person pose estimation(SPPE) to extract high-quality single-person pose regions from inaccurate human candidate frames. Global matching method is used to transform the key point prediction problem into the optimal matching problem of the human body-key point graph, and solve the questions of misjudgment and error detection of pose estimation in the crowded state. Finally, depth wise separable convolution and inverted residual model are used to reduce the complexity of model, so as to improve the running speed while balancing the accuracy of the algorithm. Experiments show that the algorithm proposed in this paper not only enhance the overall performance of the multi-person posture estimation network in the crowded state, but also improves the running speed significantly, which further confirms the effectiveness and competitiveness of this algorithm.

Keywords

• Human pose estimation
• symmetric transformation
• depthwise separable convolution
• global matching method