A novel method for automated human behavior segmentation
Xing Weiwei, Weiqiang Wang, Peng Bao, Liya Sun, Tong Leiming · Computer Animation and Virtual Worlds · 2016
Abstract In order to conveniently classify, retrieve, and synthesize human motion, motion capture (MoCap) data need to be properly segmented into distinct behaviors. In this paper, we propose a novel automated segmentation method based onposture histograms in sliding window. Firstly, a set of new posture features are proposed and defined to construct theposture histogram, which is a new compact representation of behavioral features. Then, by executing thesliding window, especially in this paper, the behavior features are analyzed in subsequence level to reduce noise sensitivity. We open up a novel way to tune sliding window by studying steady states of human behaviors, so that conspicuous and stable behavioral features can be obtained. Finally, by analyzing the clustering property of posture histograms of the subsequences, the behavior segmentation problem is tactfully simplified to the detection ofoutlier subsequence. In particular, thelocal outlier factoralgorithm is adopted to solve outlier subsequence detection, and good results are achieved. Extensive experiments are conducted on 14 pieces of multi‐bcase, and the experimental results demonstrate that our proposed method outperforms other state‐of‐the‐art ones. Copyright © 2016 John Wiley & Sons, Ltd.