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Human gesture recognition using a simplified dynamic Bayesian network

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Abstract

In video-based human gesture recognition, it is very important to combine useful features and analyze the dynamic structure thereof as efficiently as possible. In this paper, we proposed a dynamic Bayesian network model that is a simplified model of dynamics at the level of hidden variables and employs observation windows of observation time slices for robust modeling and handling of noise and other variabilities. The proposed Simplified dynamic Bayesian network (DBN) was tested on a gesture database and an American sign language database. According to the experiments, the proposed DBN outperformed other methods: Conditional Random Fields (CRFs), conventional Bayesian Networks (BNs), DBNs, and Hidden Markov Models (HMMs). The proposed DBN achieved 98 % recognition accuracy in gesture recognition and 94.6 % in ASL recognition whereas the HMM and the CRF did 80 and 86 % in gesture recognition and 75.4 and 85.4 % in ASL (American Sign Language) recognition, respectively.

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Notes

  1. Korea University Gesture Database, http://gesturedb.korea.ac.kr.

  2. American Sign Language Database, http://www.bu.edu/asllrp/ncslgr.html.

  3. We would like to thank H.-D. Yang, the first author of [22] for providing the feature data and the results.

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Acknowledgments

This research was supported by the Implementation of Technologies for Identification, Behavior, and Location of Human based on Sensor Network Fusion Program through the Ministry of Trade, Industry and Energy (Grant No. 10041629) and the 2014 R&D Program for S/W Computing Industrial Core Technology through the Ministry of Science, ICT and Future Planning/Korea Evaluation Institute of Industrial Technology (Project No. 2014-044-023-001), Korea.

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Authors and Affiliations

  1. Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea

    Seong-Whan Lee

  2. S-1 Corporation, Seoul, Korea

    Myung-Cheol Roh

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  1. Myung-Cheol Roh
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Correspondence to Seong-Whan Lee.

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Communicated by Q. Tian.

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Roh, MC., Lee, SW. Human gesture recognition using a simplified dynamic Bayesian network. Multimedia Systems 21, 557–568 (2015). https://doi.org/10.1007/s00530-014-0414-9

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