Deep Learning for Body Parts Detection using HRNet and EfficientNet

Abstract

Human body parts detection is an important field of research in computer vision. It can serve as an essential tool in surveillance systems and used to automatically detect and moderate non-appropriate online content such as nudity, child pornography, violence, etc. In this work, we introduce a novel two-step framework to define ten body parts using joints localization. A new architecture with EfficientNet as a backbone is proposed and compared to HRNet for the first step of pose estimation. The resulting joints are then used as an input to the second step, where a set of rules is applied to connect the appropriate joints and to define each body part. The developed algorithms were tested using MPII human pose benchmark. The proposed approach achieved a very interesting performance with a 90.13% Probability of Correct Keypoint (PCK) for the pose estimation and an average of 89.80% of mean Average Precision (mAP) for the body parts detection.