TL;DR
CL-HPE is presented, a lightweight framework for competitive and efficient pose estimation that provides a practical solution in resource-constrained environments and suggests a favorable accuracy–efficiency trade-off among lightweight pose estimators.
An HGDS-YOLOv8-Pose algorithm embedded with HSAN, GL-CRM, DySample and SCAM modules for robust outdoor human pose estimation, outperforming competing methods including KAPAO-s and YOLOv8n-Pose.
This paper proposes a lightweight high-resolution pose estimation network, UG-HRNet, which significantly reduces model complexity while maintaining competitive performance, serving as a practical alternative to existing popular lightweight networks.
This paper proposes MFA-Pose, an improved YOLO11s-Pose framework that enhances contextual representation and cross-scale feature reconstruction that provides a favorable balance between pose estimation accuracy and model complexity for industrial surveillance applications.
Monocular human height estimation is of great value in intelligent security inspection and surveillance applications, but existing methods often suffer from insufficient object detection accuracy and the high computational cost of attention mechanisms. This paper proposes a dual-attention enhanced network based on impr...
Existing head pose estimation (HPE) methods focus on utilizing the information of one single image and ignore cross-viewpoint pose consistency. To remedy this, we propose a dual-branch self-correcting pose consistency network (DSPC-Net) for HPE. The idea is to construct flipped image pairs as intrinsic pose constraints...
This work proposes a novel attention-free integrated spatiotemporal architecture named Integrated Mamba-HyperGCN (Pose Magic++), which significantly improves the estimation accuracy of peripheral joints without incurring extra computational cost.
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