Open AccessFast Random‐Forest‐Based Human Pose Estimation Using a Multi‐scale and Cascade Approach
Author(s) -
Chang Ju Yong,
Nam Seung Woo
Publication year - 2013
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.13.2013.0063
Subject(s) - random forest , computer science , pose , artificial intelligence , graphics processing unit , computation , cascade , focus (optics) , pixel , computer vision , classifier (uml) , scale (ratio) , algorithm , engineering , parallel computing , physics , optics , quantum mechanics , chemical engineering
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.