標題: | Learning Atomic Human Actions Using Variable-Length Markov Models |
作者: | Liang, Yu-Ming Shih, Sheng-Wen Shih, Arthur Chun-Chieh Liao, Hong-Yuan Mark Lin, Cheng-Chung 資訊工程學系 Department of Computer Science |
關鍵字: | Atomic action learning;atomic action recognition;human behavior analysis;variable-length Markov models (VLMMs) |
公開日期: | 1-二月-2009 |
摘要: | Visual analysis of human behavior has generated considerable interest in the field of computer vision because of its wide spectrum of potential applications. Human behavior can be segmented into atomic actions, each of which indicates a basic and complete movement. Learning and recognizing atomic human actions are essential to human behavior analysis. In this paper, we propose a framework for handling this task using variable-length Markov models (VLMMs). The framework is comprised of the following two modules: a posture labeling module and a VLMM atomic action learning and recognition module. First, a posture template selection algorithm, based on a modified shape context matching technique, is developed. The selected posture templates form a codebook that is used to convert input posture sequences into discrete symbol sequences for subsequent processing. Then, the VLMM technique is applied to learn the training symbol sequences of atomic actions. Finally, the constructed VLMMs are transformed into hidden Markov models (HMMs) for recognizing input atomic actions. This approach combines the advantages of the excellent learning function of a VLMM and the fault-tolerant recognition ability of an HMM. Experiments on realistic data demonstrate the efficacy of the proposed system. |
URI: | http://dx.doi.org/10.1109/TSMCB.2008.2005643 http://hdl.handle.net/11536/7674 |
ISSN: | 1083-4419 |
DOI: | 10.1109/TSMCB.2008.2005643 |
期刊: | IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS |
Volume: | 39 |
Issue: | 1 |
起始頁: | 268 |
結束頁: | 280 |
顯示於類別: | 期刊論文 |