Research on Multi-Person Pose Estimation Based on YOLO and Decoupled Multi-Level Feature Layers Fusion
Bin Zheng, He Zhang, Jin Lu · 2023
Multi-person pose estimation is fundamental research in the fields of AIGC, multimedia understanding,virtual reality, human-computer interaction, etc. Existing algorithms have problems such as large computational complexity, low accuracy, and an inability to effectively predict occlusions, making them difficult to deploy on devices with limited computational resources. This paper introduces coordinate attention mechanisms within the YOLOv7tiny framework, improves the OKS loss function tailored for pose estimation tasks, and proposes the FDPose method that decouples bounding box detection and pose regression tasks by fusing different feature layers. Experimental results show that compared with the original algorithm, the standard version FDPose-n used in this paper reduces the number of parameters by 14.8% while improving AP by 9.7%. In comparison with some similarly sized coordinate regression based bottom-up algorithms, FDPose-n achieves higher AP than Associative Embedding, DeepPose, YOLOv5s-pose, YOLOv8n-pose, etc. We use the CrowdPose dataset as the training set and without any pre-trained weights.