文章基本信息

标题：Fast Random-Forest-Based Human Pose Estimation Using a Multi-scale and Cascade Approach
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作者：Chang, Ju Yong ; Nam, Seung Woo
期刊名称：ETRI Journal
印刷版ISSN：1225-6463
电子版ISSN：2233-7326
出版年度：2013
卷号：35
期号：6
页码：949-959
DOI：10.4218/etrij.13.2013.0063
语种：English
出版社：Electronics and Telecommunications Research Institute
摘要：Since the recent launch of Microsoft Xbox Kinect, research on 3D human pose estimation has attracted a lot of attention in the computer vision community. Kinect shows impressive estimation accuracy and real-time performance on massive graphics processing unit hardware. In this paper, we focus on further reducing the computation complexity of the existing state-of-the-art method to make the real-time 3D human pose estimation functionality applicable to devices with lower computing power. As a result, we propose two simple approaches to speed up the random-forest-based human pose estimation method. In the original algorithm, the random forest classifier is applied to all pixels of the segmented human depth image. We first use a multi-scale approach to reduce the number of such calculations. Second, the complexity of the random forest classification itself is decreased by the proposed cascade approach. Experiment results for real data show that our method is effective and works in real time (30 fps) without any parallelization efforts.
关键词：Human pose estimation;depth data;random forest;interactive digital contents