Computationally Efficient Regression on a Dependency Graph for Human Pose Estimation

We present a hierarchical method for human pose estimation from a single still image. In our approach, a dependency graph representing relationships between reference points such as body joints is constructed and the positions of these reference points are sequentially estimated by a successive application of multidimensional output regressions along the dependency paths, starting from the root node. Each regressor takes image features computed from an image patch centered on the current node's position estimated by the previous regressor and is specialized for estimating its child nodes' positions. The use of the dependency graph allows us to decompose a complex pose estimation problem into a set of local pose estimation problems that are less complex. We design a dependency graph for two commonly used human pose estimation datasets, the Buffy Stickmen dataset and the ETHZ PASCAL Stickmen dataset, and demonstrate that our method achieves comparable accuracy to state-of-the-art results on both datasets with significantly lower computation time than existing methods. Furthermore, we propose an importance weighted boosted regression trees method for transductive learning settings and demonstrate the resulting improved performance for pose estimation tasks.