Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on Efficient Learning Units for Behavior-Recognition of People in Video

비디오에서 동체의 행위인지를 위한 효율적 학습 단위에 관한 연구

  • Kwon, Ick-Hwan (Electrics and Telecommunications Research Institute) ;
  • Hadjer, Boubenna (Dept. of Electrical and Computer Engineering, Graduate School, Pusan National University) ;
  • Lee, Dohoon (Dept. of Electrical and Computer Science Engineering, Pusan National University)
  • Received : 2016.10.08
  • Accepted : 2017.01.25
  • Published : 2017.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

Behavior of intelligent video surveillance system is recognized by analyzing the pattern of the object of interest by using the frame information of video inputted from the camera and analyzes the behavior. Detection of object's certain behaviors in the crowd has become a critical problem because in the event of terror strikes. Recognition of object's certain behaviors is an important but difficult problem in the area of computer vision. As the realization of big data utilizing machine learning, data mining techniques, the amount of video through the CCTV, Smart-phone and Drone's video has increased dramatically. In this paper, we propose a multiple-sliding window method to recognize the cumulative change as one piece in order to improve the accuracy of the recognition. The experimental results demonstrated the method was robust and efficient learning units in the classification of certain behaviors.

Keywords

References

  1. N.H. Cho, S.Z. Byun, M.K. Lee, and Y.H. Kim, "Implementation of Pedestrian Crosswalk Monitoring System for Accident Prevention," Journal of Kalinga Institute of Industrial Technology, Vol. 14, No. 4, pp. 45-50, 2016.
  2. Ministry of the Interior, CCTV Installation and Operation Status, Survey on Actual Situation and Comprehensive Support System for Personal Information Protection, 2016.
  3. S.J. Lee, H.G. Kim, J.W. Sa, and Y.W. Chung, "Real-time Moving Detection with Adaptive Generalized Method of Moments in Heterogeneous Computing Environments," Proceedings of Symposium of the Korean Institute of Communications and Information Sciences, pp. 1002-1003, 2016.
  4. T.K. Kim and J.K. Park, "Object Detection and Analysis Technology for Intelligent Video Surveillance," The Magazine of the IEEK, Vol. 39, No. 2, pp. 55-65, 2012.
  5. J.U. Lee, J.Y. Sun, and M.C. Won, "Real-Time Algorithm for Relative Position Estimation between Person and Robot Using a Monocular Camera," Transaction of the Korean Society of Mechanical Engineers, Vol. A37, No. 12, pp. 1445-1452, 2013.
  6. J.G. Ko, Y.S. Bae, J.Y. Park, and K. Park, "Technologies Trends in Image Big Data Analysis," Electronics and Telecommunications Trends, 2014.
  7. Y.Y. Nam, Y.J. Choi, and W.D. Cho, "Object Tracking: A Survey," Journal of Korean Society for Internet Information, Vol. 11, No. 1, 129-141, 2010.
  8. H.S. Chen, H.T. Chen, Y.W. Chen, and S.Y. Lee, "Human Action Recognition Using Star Skeleton," Proceeding of Association for Computing Machinery International Workshop on Video Surveillance and Sensor Networks, pp. 171-178, 2006.
  9. N.J. Kwak and T.S. Song, "Object-Action and Risk-Situation Recognition Using Moment Change and Object Size' s Ratio," Journal of Korea Multimedia Society, Vol. 17, No. 5, pp. 556-565, 2014. https://doi.org/10.9717/kmms.2014.17.5.556
  10. Lian, H. Cheng, and B.L. Lu. "Multi-view Gender Classification Using Local Binary Patterns and Support Vector Machines," Proceeding of International Symposium on Neural Networks, pp.202-209, 2006.
  11. B. Yogameena, S. Veera Lakshmi, M. Archana, and S.R. Abhaikumar, "Human Behavior Classification Using Multi-Class Relevance Vector Machine," Journal of Computer Science, pp. 1021-1026, 2010.
  12. Stauffer, Chris, W. Eric.L. Grimson, "Learning Pattern of Activity Using Real-Time Tracking." IEEE Transactions on pattern analysis and machine intelligence, Vol. 22, Issue. 8, pp. 747-757, 2000. https://doi.org/10.1109/34.868677
  13. S. Velastin, B. Boghossian, B. Lo, J. Sun, and M. Vicencio-Silva, "PRISMATICA: Toward Ambient Intelligence in Public Transport Environments," IEEE Transaction Systems, Man and Cybernetics, Vol. 35, No. 1, pp. 164-182, 2005.
  14. D.B. Nathaniel, M. Osama, P. Nikolaos, and I. Aaron, "Detection of Loitering Individuals in Public Transportation Areas," IEEE Transaction Intelligent Transportation Systems, Vol. 6, No. 2, pp. 167-177, 2005. https://doi.org/10.1109/TITS.2005.848370
  15. S. Paolo, C. Andrea, L. Marco, and M. Tommaso "An Abandoned/Removed Objects Detection Algorithm and Its Evaluation on PETS Datasets," Proceeding of IEEE International Conference on Video and Signal Based Surveillance, pp. 17, 2006.
  16. Y. Elden and J.K. Aggarwal, "Human Action Recognition with Extremities as Semantic Posture Representation," Proceeding of International Conference Computer Vision, Pattern Recognition, pp. 1-8, 2009.
  17. J. Aggarwal and Q. Cai, "Human Motion Analysis: A Review," Computer Vision and Image Understanding, pp. 428-440, 1999.
  18. A. Singh, S. Sawan, M. Hanmandlu, V. K. Madasu, B. C. Lovell, "An Abandoned Object Detection System Based on Dual Background Segmentation," Proceeding of IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 352-357, 2009.
  19. S.W. Lee, T.Y. Kim, J.H. Yoo, and J.K. Park, "Abnormal Behavior Detection Based on Adaptive Background Generation for Intelligent Video Analysis," The Institute of Electronics Engineers of Korea-Signal Processing, Vol. 48, pp. 111-121, 2011.
  20. T.T. Zin, T. Pyke, and T. Takashi, "A Markov Random Walk Model for Loitering People Detection," Proceeding of International Conference on Intelligent Information Hiding and MultiMedia Signal Processing, pp. 680-683, 2010.
  21. C.H. Huang, M.Y. Shih, Y.T. Wu, and J.H. Kao, "Loitering Detection Using Bayesian Appearance Tracker and List of Visitors," Advances in Multimedia Information Processing, pp. 906-910, 2008.
  22. E.S. Park, H.G. Lee, M.K. Yun, M.Y. Kim, J.H. Kwak, and H.K. Kim, "Loitering Behavior Detection Using Shadow Removal and Chromaticity Histogram Matching," Korea Institute of Information Security and Cryptology, Vol. 21, No. 6, pp. 159-169, 2011.
  23. A.F. Bobick and J.W. Davis, "Action Recognition using Temporal Templates," Computational Imaging and Vision Series, Vol. 9, pp. 125-146, 1997.