Keypoint Detection Method for Single Person Gymnastics Actions Based on Multi-Scale Incremental Learning
Keypoint detection of human body is a hot research area in computer vision.At present there exist some problems for keypoint detection in gymnastics actions,such as insufficient detection accuracy and lack of capability to de-tect detailed body parts.In order to improve the detection accuracy,this paper proposes a multi-resolution network that has a larger receptive field in the shallow layers and can utilize high-resolution channel to enhance the extraction of detailed fea-tures.To achieve the detection of keypoints of hands and feet,an incremental learning network is designed.The network fuses the shallow features of the multi-resolution network and computes deep features using a gymnastics actions self-built dataset,so that the detection ability of keypoints on hands and feet is improved.Finally,the output results of the two sub-networks are concated.Computer simulations demonstrate that the multi-resolution network achieves an accuracy rate of 94.4%on the COCO2017 keypoint detection dataset,and the incremental learning network can accurately detect keypoints of detailed body parts with fewer training data.
human keypoint detectiongymnastics actionsmulti-resolution networkincremental learningweight fusion
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