Local Optimization Networks for Multi-View Multi-Person Human Posture Estimation

With the growing applicability of multi-view multi-person 3D human pose estimation across diverse scenarios, the impact of external environmental factors and occlusion on accuracy has garnered substantial attention. In this research, we introduce a novel approach to multi-view multi-person 3D human pose estimation, leveraging a localized optimization strategy. Specifically, our method enhances the interplay of feature information from different channels and fine-tunes the optimal feature weights to capture intricate dependencies among joints. This refinement leads to improved accuracy in handling external environmental factors. Experimental evaluations were conducted on two prominent benchmark datasets, namely Campus and Shelf. The proposed method achieved a remarkable performance, with a Percentage of Correct Parts (PCP) score of 97.4% and 98.2% for the Campus and Shelf datasets, respectively.