Hierarchical pose net: spatial hierarchical body tree driven multi-person pose estimation

In this paper, we explore multi-level semantic information of human body structure and propose a paradigm for bottom-up multi-person pose estimation. To represent the multi-level semantic body structure, we define a Spatial Hierarchical Body Tree (SHBT) that encodes the location and association information of the body center, parts, and joints for each human instance. This encoding approach assists in associating joints to each human instance, and the multi-level form is suitable for handling cases of partial human body occlusion. To apply the spatial hierarchical body tree to multi-person pose estimation, we build Hierarchical Pose Net(Heap-net) by inheriting the topology of the SHBT. This Heap-net explicitly defines the corresponding output order and the feature fusion aggregation. Furthermore, we propose a shared filters spatial pyramid module, which consists of a multi-branches dilation convolution module with shared filters and a max-out activation, to alleviate the effect of a wide range of human scale. To verify the effectiveness of our model, we conduct experiments on the MSCOCO keypoints detection validation and test set. The experimental results are comparable to the previous bottom-up multi-person pose estimation methods.