ReferPose: Distance Optimization-Based Reference Learning for Human Pose Estimation and Monitoring

Existing deep learning models for human pose estimation (HPE) have shown satisfactory performance in monitoring human actions. However, they usually face a dilemma between complexity and accuracy. To address this challenge, we propose an effective reference learning method for HPE (namely ReferPose), which is based on a new distance optimization strategy. Specifically, we utilize a reference model for pose learning and representation. The pose representation learned from the entire database is merged into the reference model, providing continuous reference learning guidance for an in-training model. In addition, we design a new cosine annealing-based reference guidance for temporal denoising and further develop a distance optimization strategy to provide joint guidance from pose knowledge, model representation, and temporal experience. Experimental results on two benchmark databases and a human fall monitoring system demonstrate that our ReferPose not only achieves promising accuracy improvement compared with several representative HPE models, but also offers low cost and high efficiency.