关键词: 人体姿态, 数据集, 随机森林, Poisson过程, 深度图像

Abstract: The human pose estimation system which uses the random forest as classifier has a problem about taking up too big memory footprint, so this paper puts forward an optimization random forest model to solve the problem above. The new model introduces the Poisson process and combines it with the depth information to form a filter before Bootstrap sampling, and then filter the original training dataset, moving the pixel sample which not plays a positive role away. After that the goal of refactor the training dataset is achieved. So the insufficient about repeated sampling and the weak representative of random forest can be improved. And the experimental results show this optimization is effective, reducing the time and space complexity of the system greatly, and makes the system more general.

Key words: human pose, dataset, random forest, Poisson process, depth image