A novel method for automated human behavior segmentation

作者：Xing Weiwei^*; Wang Weiqiang; Bao Peng; Sun Liya; Tong Leiming

来源：Computer Animation and Virtual Worlds, 2016, 27(5): 501-514.

DOI：10.1002/cav.1690

• motion capture
• human behavior segmentation
• posture histogram
• sliding window
• posture features
• outlier detection

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 on posture histograms in sliding Firstly, a set of new posture features are proposed and defined to construct the posture histogram, which is a new compact representation of behavioral features. Then, by executing the sliding 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 of outlier subsequence. In particular, the local outlier factor algorithm is adopted to solve outlier subsequence detection, and good results are achieved. Extensive experiments are conducted on 14 pieces of multi-behavior MoCap data from the CMU database,(dagger) and the experimental results demonstrate that our proposed method outperforms other state-of-the-art ones.

• 出版日期2016-10
• 单位北京交通大学