Full-Body Human Pose Estimation from Monocular Video Sequence via Multi-dimensional Boosting Regression

In this work, we propose a scheme to estimate two-dimensional full-body human poses in a monocular video sequence. For each frame in the video, we detect the human region using a support vector machine, and estimate the full-body human pose in the detected region using multi-dimensional boosting regression. For the human pose estimation, we design a joints relationship tree, corresponding to the full hierarchical structure of joints in a human body. Further, we make a complete set of spatial and temporal feature descriptors for each frame. Utilizing the well-designed joints relationship tree and feature descriptors, we learn a hierarchy of regressors in the training stage and employ the learned regressors to determine all the joint’s positions in the testing stage. As experimentally demonstrated, the proposed scheme achieves outstanding estimation performance.