Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

Object Segmentation/Detection through learned Background Model and Segmented Object Tracking Method using Particle Filter

배경 모델 학습을 통한 객체 분할/검출 및 파티클 필터를 이용한 분할된 객체의 움직임 추적 방법

  • Lim, Su-chang (Department of Computer Science, Sunchon National University) ;
  • Kim, Do-yeon (Department of Computer Engineering, Sunchon National University)
  • Received : 2016.06.17
  • Accepted : 2016.06.30
  • Published : 2016.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

In real time video sequence, object segmentation and tracking method are actively applied in various application tasks, such as surveillance system, mobile robots, augmented reality. This paper propose a robust object tracking method. The background models are constructed by learning the initial part of each video sequences. After that, the moving objects are detected via object segmentation by using background subtraction method. The region of detected objects are continuously tracked by using the HSV color histogram with particle filter. The proposed segmentation method is superior to average background model in term of moving object detection. In addition, the proposed tracking method provide a continuous tracking result even in the case that multiple objects are existed with similar color, and severe occlusion are occurred with multiple objects. The experiment results provided with 85.9 % of average object overlapping rate and 96.3% of average object tracking rate using two video sequences.

실시간영상에서 객체의 분할 및 추적은 침입자 감시와 로봇의 물체 추적, 증강현실의 객체 추적등 다양한 분야에서 사용되고 있다. 본 논문에서는 초기 입력 영상의 일부를 학습하여 배경모델로 제작한 후, 배경제거 방법을 이용하여 움직이는 객체의 분할을 통해 객체를 검출하였다. 검출된 객체의 영역을 기반으로 HSV 색상히스토그램과 파티클 필터를 이용하여 객체의 움직임을 추적하는 방법을 제안한다. 제안한 분할 방법은 평균 배경모델을 이용한 방법보다 주변환경 변화의 영향을 적게 받으며, 움직이는 객체의 검출 성능이 더욱 우수하였다. 또한 단일 객체 및 다수의 객체가 존재하는 환경에서 추적 객체가 유사한 색상 객체와 겹치는 경우, 추적 객체의 영역 절반 이상이 가려지는 경우에도 지속적으로 추적하는 결과를 얻을 수 있었다. 2개의 비디오 영상을 사용한 실험결과는 평균 중첩율 85.9%, 추적률 96.3%의 성능을 보여준다.

Keywords

References

  1. T. W. Jang, Y. T. Shin, and J. B. Kim, "A Study on the Object Extraction and Tracking System for Intelligent Surveillance," The Journal of The Korean Institute of Communication Sciences, vol. 38, no. 7, pp. 358-362, Jul. 2013.
  2. H. H. Lee, et al, "Robust Object Tracking in Mobile Robots using Object Features and On-line Learning based Particle Filter," Journal of Institute of Control, Robotics and Systems, vol. 18, no. 6, pp. 562-570, Jun. 2012. https://doi.org/10.5302/J.ICROS.2012.18.6.562
  3. D. Wagner, et al, "Real-time detection and tracking for augmented reality on mobile phones," Visualization and Computer Graphics, IEEE Transactions, vol. 16, no. 3, pp. 355-368, May. 2010. https://doi.org/10.1109/TVCG.2009.99
  4. Y. J. Chai, et al, "Realtime user interface using particle filter with integral histogram," in Digest of Technical Papers International Conference on Consumer Electronics, Las Vegas: NV, pp. 245-246, 2010.
  5. Z. Zivkovic, "Improved adaptive Gaussian mixture model for background subtraction," in Proceeding of the 17th International Conference on Pattern Recognition(ICPR), Cambridge: UK, vol. 2, pp. 28-31. 2004.
  6. J. H. Lee, S. W. Cho, J. M. Kim, and S. T. Chung, "Layered Object Detection using Adaptive Gaussian Mixture Model in the Complex and Dynamic Environment," Journal of Korean Institute of Intelligent Systems, vol. 18, no. 3, pp. 387-391, Jun. 2008. https://doi.org/10.5391/JKIIS.2008.18.3.387
  7. A. Merin, and J. Anitha, "A survey of moving object segmentation methods," International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE), vol. 1, Oct. 2012.
  8. M. Piccardi, "Background subtraction techniques: a review.," in IEEE International Conference on Systems, Man and Cybernetics, Hague: NLD, pp. 3099-3104, 2004.
  9. I. Leichter, M. Lindenbaum, and E. Rivlin, "Mean shift tracking with multiple reference color histograms," Computer Vision and Image Understanding, vol. 114, no. 3, pp. 400-408, Mar. 2010. https://doi.org/10.1016/j.cviu.2009.12.006
  10. C. J. Seo. "A Study on Multi Target Tracking using HOG and Kalman Filter," The Transaction of The Institute of Electrical Engineers, vol. 64, no. 3, pp. 187-192, Sep. 2015.
  11. H. S. Yang, and Y. J. Choi, "Active Object Tracking based on Pan-tilt-zoom (PTZ) Camera for Smart Surveillance System," Journal of KISS : Software and Applications, vol. 38, no. 3, pp. 134-143, Mar. 2011.
  12. H. B. Kim, K. E. Ko, J. S. Kang, and K. B. Sim, "Specified Object Tracking in an Environment of Multiple Moving Objects using Particle Filter," The Journal of Korean Institute of Intelligent Systems, vol. 21, no. 1, pp. 106-111, Feb. 2011. https://doi.org/10.5391/JKIIS.2011.21.1.106
  13. K. Nummiaro, E. Koller-Meier, and L. Van Gool, "An adaptive color-based particle filter," Image and vision computing, vol. 21, no. 1, pp. 99-110, Jan. 2003. https://doi.org/10.1016/S0262-8856(02)00129-4
  14. B. C. Ko, J. Y. Nam, and J. Y. Kwak, "Object tracking using particle filters in moving camera," The Journal of Korean Institute of Communications and Information Sciencesm, vol. 37, no. 5A, pp. 375-387, 2012. https://doi.org/10.7840/KICS.2012.37A.5.375
  15. M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking," IEEE Transactions on Signal Processing, vol. 50, no. 2, pp. 174-188, 2002. https://doi.org/10.1109/78.978374
  16. F. Yin, D. Makris, and S. A. Velastin, "Performance evaluation of object tracking algorithms," in IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, Rio De Janeiro: BRA, p. 25, 2007.

Cited by

  1. Implementation of an improved real-time object tracking algorithm using brightness feature information and color information of object vol.22, pp.5, 2016, https://doi.org/10.9708/jksci.2017.22.05.021