Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Multiple-Background Model-Based Object Detection for Fixed-Embedded Surveillance System

고정형 임베디드 감시 카메라 시스템을 위한 다중 배경모델기반 객체검출

  • Park, Su-In (Department of Robot Engineering, Kyungpook National University) ;
  • Kim, Min Young (School of Electronics Engineering, Kyungpook National University)
  • Received : 2015.08.23
  • Accepted : 2015.10.21
  • Published : 2015.11.01
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Abstract

Due to the recent increase of the importance and demand of security services, the importance of a surveillance monitor system that makes an automatic security system possible is increasing. As the market for surveillance monitor systems is growing, price competitiveness is becoming important. As a result of this trend, surveillance monitor systems based on an embedded system are widely used. In this paper, an object detection algorithm based on an embedded system for a surveillance monitor system is introduced. To apply the object detection algorithm to the embedded system, the most important issue is the efficient use of resources, such as memory and processors. Therefore, designing an appropriate algorithm considering the limit of resources is required. The proposed algorithm uses two background models; therefore, the embedded system is designed to have two independent processors. One processor checks the sub-background models for if there are any changes with high update frequency, and another processor makes the main background model, which is used for object detection. In this way, a background model will be made with images that have no objects to detect and improve the object detection performance. The object detection algorithm utilizes one-dimensional histogram distribution, which makes the detection faster. The proposed object detection algorithm works fast and accurately even in a low-priced embedded system.

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References

  1. S. H. Hong, "IP camera market and technology trend in image surveillance industries," Journal of Korea Institute of Information Security and Cryptology (in Korean), vol. 20, no. 3, pp. 18-23, 2010.
  2. B. J. Jeon, "Domestic and international trend of intelligent CCTV image surveillance industries," Journal of Telecommunications Technology Association (in Korean), vol. 142, pp. 50-55, Aug. 2012.
  3. J. Bescos, "Real-time shot change detection over online MPEG-2 video," IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 4, pp. 475-484, 2004. https://doi.org/10.1109/TCSVT.2004.825546
  4. L. Wang, L. Wang, Q. Zhuo, H. Xiao, and W. Wang, "Adaptive eigenbackground for dynamic bckground modeling," Intelligent Computing in Signal Processing and Pattern Recognition, Lecture Notes in Control and Information Sciences, vol. 345, pp. 670-675, 2006.
  5. I. Haritaoglu, D. Harwood, and L. S. Davis, "W4: realtime surveillance of people and their activities," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 809-830, Aug. 2000. https://doi.org/10.1109/34.868683
  6. H. Jiang, J. Wang, Z. Yuan, T. Liu, N. Zheng, and S. Li, "Automatic salient object segmentation based on context and shape prior," Proc. of British Machine Vision Conference, pp. 110.1-110.12, 2011.
  7. B. Zheng, X. Xu, Y. Dai, and Y. Lu, "Object tracking algorithm based on combination of dynamic template matching and Kalman filter," Proc. of IEEE 4th International Conference on Intelligent Human-Machine Systems and Cybernetics, vol. 2, pp. 136-139, 2012.
  8. Y. Choi, K. Kwon, J. Kim, K. Na, and S. Lee, "Real time omni-directional object detection using background subtraction of fisheye image," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 21. no. 8, pp. 766-772, 2015. https://doi.org/10.5302/J.ICROS.2015.15.0003
  9. S. Kang, K. Heo, and Y. Yang, "Development of a drowsiness detection system using a histogram for vehicle safety," Journal of Institute of Control, Robotics and Systems, vol. 21. no. 2, pp. 102-107, 2015. https://doi.org/10.5302/J.ICROS.2015.14.9004
  10. I. Haritaoglu, D. Harwood, and L. S. Davis, "A fast background scene modeling and maintenance for outdoor surveillance," Proc. of IEEE 15th International Conference on Pattern Recognition, vol. 4, pp. 179-183, 2000.
  11. D. S. Lee, "Effective Gaussian mixture learning for video background subtraction," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 5, pp. 827-832, 2005. https://doi.org/10.1109/TPAMI.2005.102
  12. Z. Zivkovic, "Improved adaptive Gaussian mixture model for background subtraction," Proc. of IEEE 17th International Conference on Pattern Recognition, vol. 2, pp. 28-31, 2004.