Hierarchical Topology Meets Temporal Occupancy: A Comprehensive Model for Multi-Person Pose Tracking

Existing approaches to multi-person pose tracking often suffer from low-confidence detections due to inter-instance and intra-instance occlusions, as well as non-canonical poses. In this work, we propose a novel solution by addressing two critical aspects: incomplete joint temporal dependencies and spatio-temporal voxelization. First, we introduce a method for extracting hierarchical relationships between joints based on human dynamics, enabling the model to reason about occlusions within the spatial topology of the human body. This hierarchical approach tackles incomplete joint visibility by leveraging the interdependencies between joints in both space and time. Second, we present a spatio-temporal occupancy network for multi-person pose tracking. By stacking 2D pose data over time to create a spatio-temporal voxel grid, the model captures temporal relationships between instances and joints, enhancing spatio-temporal correlations and learning keypoint distributions under occlusions or non-canonical poses. Extensive experiments on the PoseTrack2017, PoseTrack2018, and PoseTrack21 dataset demonstrate that our method improves multi-person pose tracking performance, achieving state-of-the-art mAP.