Browse > Article

Real-Time Object Tracking and Segmentation Using Adaptive Color Snake Model  

Seo Kap-Ho (Department of Electrical Engineering and Computer Science, KAIST)
Shin Jin-Ho (Department of Mechatronics Engineering, Dong-Eui University)
Kim Won (Department of Electrical Engineering and Computer Science, KAIST)
Lee Ju-Jang (Department of Electrical Engineering and Computer Science, KAIST)
Publication Information
International Journal of Control, Automation, and Systems / v.4, no.2, 2006 , pp. 236-246 More about this Journal
Abstract
Motion tracking and object segmentation are the most fundamental and critical problems in vision tasks such as motion analysis. An active contour model, snake, was developed as a useful segmenting and tracking tool for rigid or non-rigid objects. In this paper, the development of new snake model called 'adaptive color snake model (ACSM)' for segmentation and tracking is introduced. The simple operation makes the algorithm runs in real-time. For robust tracking, the condensation algorithm was adopted to control the parameters of ACSM. The effectiveness of the ACSM is verified by appropriate simulations and experiments.
Keywords
Active contours; condensation algorithm; object tracking; image segmentation;
Citations & Related Records

Times Cited By Web Of Science : 7  (Related Records In Web of Science)
Times Cited By SCOPUS : 10
연도 인용수 순위
1 M. Kass, A. Witkin, and D. Terzopoulos, 'Snake: Active contour models,' Int. J. Computer Vision, vol. 1, pp. 321-331, 1988   DOI
2 A. Blake and A. Yuille, Active Vision, MIT Press, Cambridge, 1992
3 T. Gevers, S. Ghebreab, and A. W. M. Smeulders, 'Color invariant snakes,' Proc. of Ninth British Machine Vision Conf., pp. 659-670, 1998
4 K. Seo and J. Lee, 'Object tracking using adaptive color snake model,' Proc. of IEEE/ASME International Conference on Advanced Intelligent Mechatronics, vol. 2, pp. 1406-1410, 2003
5 G. Jang and I. Kweon, 'Robust object tracking using an adaptive color model,' Proc. of IEEE Int'l Conf. Robotics & Automation, vol. 2, pp. 1677-1682, 2001
6 M. Isard and A. Blake, 'Condensation- Conditional density propagation for visual tracking,' International Journal of Computer Vision, vol. 29, no. 1, pp. 5-28, 1998   DOI
7 P. Fieguth and D. Terzopoulos, 'Color-based tracking of heads and other mobile objects at video frame rates,' Proc. of IEEE Conf. Computer Vision Pattern Recognition, pp. 21-27, 1997
8 B. Heisele, U. Krebel, and W. Ritter, 'Tracking nonrigid, moving objects based on color cluster flow,' Proc. of IEEE Conf. Computer Vision Pattern Recognition, pp. 257-260, 1997
9 K. Seo, W. Kim, C. Oh, and J. Lee, 'Face detection and facial feature extraction using color snake,' Proc. of the 2002 IEEE International Symposium on Industrial Electronics, vol. 2, pp. 457-462, 2002
10 E. B. Meier and F. Ade, 'Tracking multiple objects using the condensation algorithm,' Robotics and Autonomous Systems, vol. 34, pp. 93-105, 2001   DOI   ScienceOn
11 K. K. Sung and T. Poggio, 'Example-based learning for view-based human face detection,' IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 2, pp. 39-51, 1998
12 S. Lefevre, J. P. Gerard, A. Piron, and N. vincent, 'An extended snake model for real-time multiple object tracking,' Proc. of Int'l Workshop on Advanced Concepts for Intelligent Vision Systems, pp. 268-275, 2002
13 Y. Fu, A. T. Erdem, and A. M. Tekalp, 'Tracking visible boundary of objects using occlusion adaptive motion snake,' IEEE Trans. on Image Processing, vol. 9, no. 12, pp. 2051-2060, 2000   DOI   ScienceOn
14 M. Kirby and L. Sirovich, 'Application of the Karhunen-Loeve procedure for the characterization of human faces,' IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 16, pp. 689-700, 1994   DOI   ScienceOn
15 A. Doulamis, N. Doulamis, K. Ntalianis, and S. Kollias, 'An efficient fully unsupervised video object segmentation scheme using an adaptive neural-network classifier architecture,' IEEE Trans. on Neural Network, vol. 14, no. 3, pp. 616-630, 2003   DOI   ScienceOn
16 B. Bascle and R. Deriche, 'Region tracking through image sequences,' Proc. of Fifth IEEE Int'l Conf. Computer Vision, pp. 302-307, 1995
17 A. Amini, T. Weymouth, and R. Jain, 'Using dynamic programming for solving variational problems in vision,' IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 12, pp. 855-867, 1990   DOI   ScienceOn
18 D. Terzopoulos and K. Waters, 'Analysis and synthesis of facial image sequences using physical and anatomical models,' IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 15, no. 6, pp. 569-579, 1993   DOI   ScienceOn
19 W. Kim, C. Lee, and J. Lee, 'Tracking moving object using snake's jump based on image flow,' Mechatronics, vol. 11, pp. 199-226, 2001   DOI   ScienceOn
20 G. Sapiro, 'Color snakes,' Computer Vision and Image Understanding, vol. 68, no. 2, pp. 247-253, 1997   DOI   ScienceOn
21 M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis, and Machine Vision, 2nd edition, PWS Publishing, 1998
22 D. DeCarlo and D. Metaxas, 'The integration of optical flow and deformable models: Applications to human face shape and motion estimation,' Proc. of IEEE Computer Vision and Pattern Recognition, pp. 231-238, 1996
23 C. Kervrann and F. Heitz, 'Robust tracking of stochastic deformable models in long image sequences,' Proc. Int'l Conf. Image Processing, vol. 3, pp. 88-92, 1994
24 N. Peterfreund, 'Robust tracking of position and velocity with kalman snakes,' IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 21, no. 6, pp.564-569, 1999   DOI   ScienceOn