Multi-person Pose Estimation Research Articles

Multi-Person Pose Estimation via Multi-Layer Fractal Network and Joints Kinship Pattern.

We propose an effective method to boost the accuracy of multi-person pose estimation in images. Initially, the three-layer fractal network was constructed to regress multi-person joints location heatmap that can help to enhance an image region with receptive field and capture more joints local-contextual feature information, thereby producing keypoints heatmap intermediate prediction to optimize human body joints regression results. Subsequently, the hierarchical bi-directional inference algorithm was proposed to calculate the degree of relatedness (call it Kinship) for adjacent joints, and it combines the Kinship between adjacent joints with the spatial constraints, which we refer to as joints kinship pattern matching mechanism, to determine the best matched joints pair. We iterate the above-mentioned joints matching process layer by layer until all joints are assigned to a corresponding individual. Comprehensive experiments demonstrate that the proposed approach outperforms the state-of-the-art schemes and achieves about 1% and 0.6% increase in mAP on MPII multi-person subset and MSCOCO 2016 keypoints challenge.

Combining fractal hourglass network and skeleton joints pairwise affinity for multi-person pose estimation

Human pose estimation, especially multi-person pose estimation, is vital for understanding human abnormal behavior. In this paper, we develop a fractal hourglass model to automatically regress human body joints, and propose a layered double-way inference algorithm to calculate the affinity between neighboring skeleton joints. Firstly, the original hourglass resident unit was replaced and the candidate skeleton joints location heatmap regression process was described. And then, we determine the specific body joints location and optimize the regression results. Next, the double-way conditional probabilities between adjacent joints is defined as joints pairwise affinity, and is applied to match adjacent human body part. What’s more, we adopt the spatial distance constraint to refine body joints matching result. Finally, we connect the best matching joints-pair, and iterate the process until all candidate joints are assigned into individual. Extensive experiments on the MPII multi-person subset and the COCO 2016 keypoints challenge show the effectiveness of our method, outperforming the second best method (Associative Embedding) by 0.45 and 1.20%.