Multi-Person Pose Estimation with Enhanced Feature Aggregation and Selection

• URL: http://arxiv.org/abs/2003.10238v1
• Date: Fri, 20 Mar 2020 08:33:25 GMT
• Title: Multi-Person Pose Estimation with Enhanced Feature Aggregation and Selection
• Authors: Xixia Xu, Qi Zou, Xue Lin
• Abstract summary: We propose a novel Enhanced Feature Aggregation and Selection network (EFASNet) for multi-person 2D human pose estimation. Our method can well handle crowded, cluttered and occluded scenes. Comprehensive experiments demonstrate that the proposed approach outperforms the state-of-the-art methods.
• Score: 33.15192824888279
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel Enhanced Feature Aggregation and Selection network (EFASNet) for multi-person 2D human pose estimation. Due to enhanced feature representation, our method can well handle crowded, cluttered and occluded scenes. More specifically, a Feature Aggregation and Selection Module (FASM), which constructs hierarchical multi-scale feature aggregation and makes the aggregated features discriminative, is proposed to get more accurate fine-grained representation, leading to more precise joint locations. Then, we perform a simple Feature Fusion (FF) strategy which effectively fuses high-resolution spatial features and low-resolution semantic features to obtain more reliable context information for well-estimated joints. Finally, we build a Dense Upsampling Convolution (DUC) module to generate more precise prediction, which can recover missing joint details that are usually unavailable in common upsampling process. As a result, the predicted keypoint heatmaps are more accurate. Comprehensive experiments demonstrate that the proposed approach outperforms the state-of-the-art methods and achieves the superior performance over three benchmark datasets: the recent big dataset CrowdPose, the COCO keypoint detection dataset and the MPII Human Pose dataset. Our code will be released upon acceptance.

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