Browse > Article
http://dx.doi.org/10.3745/KIPSTB.2002.9B.3.359

A Study on Effective Moving Object Segmentation and Fast Tracking Algorithm  

Jo, Yeong-Seok (극동정보대학 전산정보처리과)
Lee, Ju-Sin (청주대학교 첨단공학부)
Publication Information
The KIPS Transactions:PartB / v.9B, no.3, 2002 , pp. 359-368 More about this Journal
Abstract
In this paper, we propose effective boundary line extraction algorithm for moving objects by matching error image and moving vectors, and fast tracking algorithm for moving object by partial boundary lines. We extracted boundary line for moving object by generating seeds with probability distribution function based on Watershed algorithm, and by extracting boundary line for moving objects through extending seeds, and then by using moving vectors. We processed tracking algorithm for moving object by using a part of boundary lines as features. We set up a part of every-direction boundary line for moving object as the initial feature vectors for moving objects. Then, we tracked moving object within current frames by using feature vector for the previous frames. As the result of the simulation for tracking moving object on the real images, we found that tracking processing of the proposed algorithm was simple due to tracking boundary line only for moving object as a feature, in contrast to the traditional tracking algorithm for active contour line that have varying processing cost with the length of boundary line. The operations was reduced about 39% as contrasted with the full search BMA. Tracking error was less than 4 pixel when the feature vector was $(15\times{5)}$ through the information of every-direction boundary line. The proposed algorithm just needed 200 times of search operation.
Keywords
Segmentation; Boundary Line Detection; BMA; Tracking;
Citations & Related Records
연도 인용수 순위
  • Reference
1 P. Allen, B. Yoshimi, and A. Timcenko, Hand-Eye Coordination for Robotics Tracking and Grasping, K. Hashimoto, ed., Visual Serving, World Scientific, pp.33-70, 1994
2 S. Hutchinson, G.D. Hager, and P. Corke, A Tutorial Introduction to Visual Servo Control, IEEE Trans. Robotics and Automation, Vol.12, No.5, pp.651-670, 1996   DOI   ScienceOn
3 A.F. Bobick and A.D. Wilson, A State-Based Technique for the Summarization of Recognition of Gesture, Proc. Int l Conf.Computer Vision, pp.382-388, 1995   DOI
4 T. Darrell, B. Moghaddam, and A. Pentland, Active Face Tracking and Pose Estimation in an Interactive Room, Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp.67-72, 1996   DOI
5 D. Gavrila and L. Davis, Tracking Humans in Action : A 3D Model-Based Approach, Proc. Image Understanding Workshop, pp.737-746, 1996
6 R. Howarth and H. Buxton, Visual Surveillance Monitoring and Watching, Proc. European Conf. Computer Vision, Vol.2, pp.321-334, 1996   DOI
7 E. Bardinet, L. Cohen, and N. Ayache, Tracking Medical 3D Data With a Deformable Parametric Model, Proc. European Conf. Computer Vision, pp.317-328, 1996   DOI
8 P. Shi, G. Robinson, T. Constable, A. Sinusas, and J. Duncan, A Model-Based Integrated Approach to Track Myocardial Deformation Using Displacement and Velocity Constraints, Proc. Int l Conf. Computer Vision, pp.687-692, 1995   DOI
9 N. Papanikolopoulos, P. Khosla, and T. Kanade, Visual Tracking of a Moving Target by a Camera Mounted on a Robot : A Combination of Control and Vision, IEEE Trans. Robotics and Automation, Vol.9, No.1, pp.14-35, 1993   DOI   ScienceOn
10 E. Dickmanns and V. Graefe, Dynamic Monocular Machine Vision, Machine Vision and Applications, Vol.1, pp.223-240, 1988   DOI
11 조영석, 이주신, '부분 외곽선 정보를 이용한 이동물체 추적 알고리즘,' 정보처리학회논문집, 제8-B권 제5호, pp.539-548, 2001   과학기술학회마을
12 Gregory D. Hager, Peter N. Belhumeur, 'Efficient Region Tracking with Parametric Models of Geometry and Illumination,' IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Vol.20, No.10, pp.1025-1039, 1998   DOI   ScienceOn
13 M. Jayakumar and Ravi N. Banavar, 'Risk-Sensitive Filters for Recursive Estimation of Motion from Images,' IEEE TPAMI, Vol.20, No.6, pp.659-666, June, 1998   DOI   ScienceOn
14 Paul L. Rosin, 'Thresholding for Change Detection,' technical report ISTR-97-01, pp.1-20, 1997
15 고선영, '영역확장 기반추적을 이용한 효율적인 3차원 의료영상 분할기법,' 한국과학기술원 석사학위논문, 2000
16 Pqul L. Rosin, 'Thresholding for Change Detection,' Brunel University
17 T. Frank, M. Haag, H. Kolling, and H.H. Nagel, Tracking of Occluded Vehicles in Traffic Scenes, Proc. European Conf. Computer Vision, Vol.2, pp.485-494, 1996   DOI
18 R.C. Harrell, D.C. Slaughter, and P.D. Adsit, A Fruit-Tracking System for Robotic Harvesting, Machine Vision and Applications, Vol.2, pp.69-80, 1989   DOI
19 D. Reynard, A. Wildenberg, A. Blake, and J. Marchant, Learning Dynamics of Complex Motions From Image Sequences, Proc. European Conf. Computer Vision, Vol.1, pp.357-368, 1996
20 E. Boyer, Object Models From Contour Sequences, Proc. European Conf. Computer Vision, pp.687-692, 1995   DOI
21 L. Shapiro, Affine Analysis of Image Sequences, Cambridge Univ. Press, England : Cambridge, Cambridge Univ. Press, 1995
22 C. Tomasi and T. Kanade, Shape and Motion From Image Streams Under Orthography : A Factorization Method, Int l J. Computer Vision, Vol.9, No.2, pp.137-154, 1992   DOI
23 채옥삼, 경태원, '이동물체 분리를 위한 Seed 선정 및 영역확장 알고리즘에 관한 연구,' 신호처리학회논문집, 2001