Human behavior recognition system based on 3-dimensional clustering methods

Maimaitimin Maierdan, Keigo Watanabe, Shoichi Maeyama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

In this paper, a Hidden Markov Model (HMM) approach is introduced for recognizing human behaviors. Two main points are discussed in this approach: first is the application of HMM to the recognition system of human behaviors, and second is the effectiveness comparison of K-means and fuzzy C-means clustering algorithms. Three sample human behaviors are defined and the corresponded 3D data are collected using the Microsoft Kinect sensor (3D sensor). During these processing, we discuss the difference of k-means and fuzzy c-means clustering algorithms, and also we can see the results impacted by different clustering algorithms, the effectiveness of both clustering methods is shown through demonstrating the performance of our recognition system with HMM.

Original languageEnglish
Title of host publicationInternational Conference on Control, Automation and Systems
Pages1133-1137
Number of pages5
DOIs
Publication statusPublished - 2013
Event2013 13th International Conference on Control, Automation and Systems, ICCAS 2013 - Gwangju, Korea, Republic of
Duration: Oct 20 2013Oct 23 2013

Other

Other2013 13th International Conference on Control, Automation and Systems, ICCAS 2013
CountryKorea, Republic of
CityGwangju
Period10/20/1310/23/13

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Keywords

  • Fuzzy C-means
  • Hidden Markov model
  • Human behavior
  • K-means
  • Recognition system

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Maierdan, M., Watanabe, K., & Maeyama, S. (2013). Human behavior recognition system based on 3-dimensional clustering methods. In International Conference on Control, Automation and Systems (pp. 1133-1137). [6704087] https://doi.org/10.1109/ICCAS.2013.6704087