计算机工程与应用2017，Vol.53Issue(2)：172-176,5.DOI:10.3778/j.issn.1002-8331.1507-0159
基于随机森林深度特征选择的人体姿态估计
Human pose estimation based on random forest depth feature selec-tion
摘要
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 represen-tative 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.关键词
人体姿态/数据集/随机森林/Poisson过程/深度图像Key words
human pose/dataset/random forest/Poisson process/depth image分类
信息技术与安全科学引用本文复制引用
朱珏钰,曹亚微,周书仁,李峰..基于随机森林深度特征选择的人体姿态估计[J].计算机工程与应用,2017,53(2):172-176,5.基金项目
湖南省教育厅资助科研项目（No.15C0283）；湖南省自然科学基金（No.12JJ6057）。 （）
