Browse > Article
http://dx.doi.org/10.5391/JKIIS.2009.19.4.586

Mobile Object Tracking Algorithm Using Particle Filter  

Kim, Se-Jin (군산대학교 제어로봇시스템공학과)
Joo, Young-Hoon (군산대학교 제어로봇시스템공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.19, no.4, 2009 , pp. 586-591 More about this Journal
Abstract
In this paper, we propose the mobile object tracking algorithm based on the feature vector using particle filter. To do this, first, we detect the movement area of mobile object by using RGB color model and extract the feature vectors of the input image by using the KLT-algorithm. And then, we get the first feature vectors by matching extracted feature vectors to the detected movement area. Second, we detect new movement area of the mobile objects by using RGB and HSI color model, and get the new feature vectors by applying the new feature vectors to the snake algorithm. And then, we find the second feature vectors by applying the second feature vectors to new movement area. So, we design the mobile object tracking algorithm by applying the second feature vectors to particle filter. Finally, we validate the applicability of the proposed method through the experience in a complex environment.
Keywords
Object tracking; Object recognition; Detection model; Snake algorithm; Particle filter;
Citations & Related Records
연도 인용수 순위
  • Reference
1 D. M. Gavrila, L. S. Davis, 'Towards 3D model based tracking and recognition of human movement: a multi view approach', Int. Workshop on Face and Gesture Recognition, Vol. 1. pp. 272-277, 1995. 6
2 G. J. Jang and I. S. Kweon. 'Robust objects tracking using an adaptive color model' Int. Conf. on Robotics and Automation. Vol. 2, pp. 1677-1682, 2001. 5
3 G. Healey and D. Slater, 'Using illumination invariant color histogram descriptors for recognition', Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp.355-360, 1994
4 U. C. Jung, S. H. Jin, X. D. Pham, J. W. Jeon, J. E. Byun, H. Kang, 'A real-time object tracking system using a particle filter', 2006 IEEE/ RSJ Int. Conf. vol. 9, pp. 2822- 2827, 2006. 10
5 K. S. Bhat, M. Saptharishi, and P. K. Khosla, 'Motion detection and segmentation using image mosaics,' in Proc. IEEE Int. Conf. Multimedia and Expo, Vol. 3, pp. 1577-1580, 2000. 6   DOI
6 B. D. Lucas and T. Kanade. 'An iterative image registration technique with an application to stereo vision', Int. Joint Conference on Artificial Intelligence, pp. 674-679, 1981, 8
7 G. D. Finlayson, B. Schiele, and J. L. Crowley. 'Comprehensive color image normalization'. 5th European Conference on Computer Vision, Vol. 1, pp. 475-490, 1998
8 C. Xu, and J. Prince, 'Snakes, shapes, and gradient vector flow', IEEE. Trans. on Image Processing, Vol. 7, No. 3, pp. 359-369, 1998   DOI   ScienceOn
9 V. I. Pavlovic, R. Sharma, and T. S. Huang, 'Visual interpretation of hand gestures for human computer interaction: A review', IEEE, Trans. on PAMI, Vol. 19, No. 7, pp. 677-695, July, 1997   DOI   ScienceOn
10 http://opencvlibrary.sourceforge.net
11 C. Garcia and G. Tziritas, 'Face detection using quantized skin color regions merging and wavelet packet analysis', IEEE Trans. on Multimedia, Vol. 1, No. 3, pp. 264-277, 1999, 9   DOI   ScienceOn
12 김세진, 주영훈 '가상 모형을 이용한 움직임 추출 알고리즘', 한국 지능 시스템 학회 Vol. 18, No. 6, pp. 731-736, 2008. 12
13 L. Matthies, T. Kanade, and R. Szeliski 'Kalman filter based algorithms for estimating depth from image sequences'. IJCV, Vol. 3, No. 3, pp. 209-236, 1989, 3   DOI
14 H. W. Sorenson, 'Least-squares estimation: From gauss to kalman', IEEE Spectrum, Vol. 7, pp. 63-68, 1970