Body part recognization base on hierarchy random forest with feature Pre-selection
Tianchu Guo, Xiaoyu Wu, Lei Yang, Xiangsheng Huang, Mingyue Yu · 2014
Pose estimation is the most important step of nature interactive between human and machine, and body part recognition is the core of pose estimation. This paper describes an improved random forests method to recognize each part of the human body. What is different from the traditional random forest structure is that the algorithm proposed in this paper provides a feature Pre - selection for examples with large feature space, making the feature set of each split node more efficient. This method not only ensures the independence between trees, but also ensures classification performance of each tree. In the combination among trees, according to the human part structure, we adopt the combined model of hierarchy forest to improve the classification performance of the forest.