Advertisement

Human Activity Recognition from Basic Actions Using Finite State Machine

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 285)

Abstract

High-level human activity recognition is an important method for the automatic event detection and recognition application, such as, surveillance system and patient monitoring system. In this paper, we propose a human activity recognition method based on FSM model. The basic actions with their properties for each person in the interested area are extracted and calculated. The action stream with related features (movement, referenced location) is recognized using the predefined FSM recognizer modeling based on rational activity. Our experimental result shows a good recognition accuracy (86.96 % in average).

Keywords

Human activity recognition Finite state machine FSM recognizer Rational activity definition 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Liu, C., & Yuen, P. C. (2010). Human action recognition using boosted EigenActions. Image and Vision Computing, 28(5), 825–835.Google Scholar
  2. 2.
    Makris, D., & Ellis, T. (2005). Learning semantic scene models from observing activity in visual surveillance.IEEE Transactions on Systems, Man and Cybernetics, 35(3), 397–408.Google Scholar
  3. 3.
    González, J., Rowe, D., Varona, J., & Roca, F. X. (2009). Understanding dynamic scenes based on human sequence evaluation.Image and Vision Computing, 27(10), 1433–1444.Google Scholar
  4. 4.
    Jan, T., Piccardi, M., Hintz, T., (2002). Detection of suspicious pedestrian behavior using modified probabilistic neural network. InProceedings of Image and Vision Computing (pp. 237–241).Google Scholar
  5. 5.
    Htike, Z.Z.; Egerton, S.; Kuang Ye Chow, “Monocular viewpoint invariant human activity recognition,” Robotics, Automation and Mechatronics (RAM), 2011 IEEE Conference on, vol., no., pp.18,23, 17-19 Sept. 2011.Google Scholar
  6. 6.
    Shariat, S.; Pavlovic, V., “Isotonic CCA for sequence alignment and activity recognition,” Computer Vision (ICCV), 2011 IEEE International Conference on, vol., no., pp.2572,2578, 6-13 Nov. 2011.Google Scholar
  7. 7.
    Gupta, S.K.; Kumar, Y.S.; Ramakrishnan, K. R., “Learning Feature Trajectories Using Gabor Filter Bank for Human Activity Segmentation and Recognition,” Computer Vision, Graphics & Image Processing, 2008. ICVGIP ‘08. Sixth Indian Conference on, vol., no., pp.111,118, 16-19 Dec. 2008.Google Scholar
  8. 8.
    Kang Li; Yun Fu, “ARMA-HMM: A new approach for early recognition of human activity,” Pattern Recognition (ICPR), 2012 21st International Conference on, vol., no., pp.1779,1782, 11-15 Nov. 2012.Google Scholar
  9. 9.
    Ping Guo; Zhenjiang Miao, “Multi-person activity recognition through hierarchical and observation decomposed HMM,” Multimedia and Expo (ICME), 2010 IEEE International Conference on, vol., no., pp.143,148, 19-23 July 2010.Google Scholar
  10. 10.
    Uddin, M.Z.; Nguyen Duc Thang; Tae-Seong Kim, “Human Activity Recognition via 3-D joint angle features and Hidden Markov models,” Image Processing (ICIP), 2010 17th IEEE International Conference on, pp.713,716, 26-29 Sept. 2010.Google Scholar
  11. 11.
    Chun Zhu, Weihua Sheng, Motion- and location-based online human daily activity recognition, Pervasive and Mobile Computing, Volume 7, Issue 2, April 2011, Pages 256-269.Google Scholar
  12. 12.
    Hongqing Fang; Lei He, “BP Neural Network for Human Activity Recognition in Smart Home,” Computer Science & Service System (CSSS), 2012 International Conference on, vol., no., pp.1034,1037, 11-13 Aug. 2012.Google Scholar
  13. 13.
    Antonio Fernández-Caballero, José Carlos Castillo, José María Rodríguez-Sánchez, “Human activity monitoring by local and global finite state machines,” Expert Systems with Applications, Volume 39, Issue 8, 15 June 2012, Pages 6982-6993.Google Scholar
  14. 14.
    L. Rodriguez-Benitez, C. Solana-Cipres, J. Moreno-Garcia, L. Jimenez-Linares, “Approximate reasoning and finite state machines to the detection of actions in video sequences, “ International Journal of Approximate Reasoning, Volume 52, Issue 4, June 2011, Pages 526-540.Google Scholar
  15. 15.
    Chun Yuan; Wei Xu, “Multi-object events recognition from video sequences using extended finite state machine,” Image and Signal Processing (CISP), 2011 4th International Congress on, vol.1, no., pp.202,205, 15-17 Oct. 2011.Google Scholar
  16. 16.
    Trinh, H.; Quanfu Fan; Jiyan Pan; Gabbur, P.; Miyazawa, S.; Pankanti, S., “Detecting human activities in retail surveillance using hierarchical finite state machine,” Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference, pp.1337,1340, 22-27 May 2011.Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  1. 1.Department of Computer Engineering, Faculty of EngineeringPrince of Songkla UniversityHatyaiThailand

Personalised recommendations