Abstract
This paper presents a feature point tracking algorithm using optical flow under the non-prior training active feature model (NPT-AFM) framework. The proposed algorithm mainly focuses on analysis of deformable objects, and provides real-time, robust tracking. The proposed object tracking procedure can be divided into two steps: (i) optical flow-based tracking of feature points and (ii) NPT-AFM for robust tracking. In order to handle occlusion problems in object tracking, feature points inside an object are estimated instead of its shape boundary of the conventional active contour model (ACM) or active shape model (ASM), and are updated as an element of the training set for the AFM. The proposed NPT-AFM framework enables the tracking of occluded objects in complicated background. Experimental results show that the proposed NPT-AFM-based algorithm can track deformable objects in real-time.
This work was supported by Korean Ministry of Science and Technology under the National Research Lab. Project, by Korean Ministry of Education under Brain Korea 21 Project, by the University Research Program in Robotics under grant DOE-R01-1344148, by the DOD/TACOM/NAC/ARC Program R01-1344-18, and by FAA/NSSA Program, R01-1344-48/49.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Haritaoglu, I., Harwood, D., Davis, L.: W-4: Real-Time Surveillance of People and Their Activities. IEEE Trans. on Pattern Analysis and Machine Intelligence, 809–830 (2000)
McKenna, S., Raja, Y., Gong, S.: Tracking Contour Objects Using Adaptive Mixture Models. Image and Vision Computing, 225–231 (1999)
Plankers, R., Fua, P.: Tracking and Modeling People in Video Sequences. Computer Vision and Image Understanding, 285–302 (2001)
Comaniciu, D., Ramesh, V., Meer, P.: Kernal-Based Object Tracking. IEEE Trans. On Pattern Analysis and Machine Intelligence, 564–577 (2003)
Wren, C., Azerbeyejani, A., Darrel, T., Pentland, A.: Pfinder: Real-Time Tracking of The Human Body. IEEE Trans. Pattern Analysis and Machine Intelligence, 780–785 (1997)
Comaniciu, D., Ramesh, V., Meer, P.: Real-Time tracking of non-rigid objects using mean shift. In: Proc. IEEE Int. Conf. Computer Vision, Pattern Recognition, pp. 142–149 (2000)
Baumberg, A.: Learning Deformable Models for Tracking Human Motion. Ph.D. Dissertation, School of Comput. Studies (1995)
Kass, M., Witkin, A., Terzopoulos, D.: Snake, Active Contour Models. International Journal of Computer Vision, 321–331 (1988)
Blake, A., Isard, M.: Active Contours. Springer, London (1998)
Cootes, T., Cooper, D., Taylor, C., Graham, J.: Active Shape Models – Their Training and Application. Comput. Image and Vision Understanding 61, 38–59 (1995)
Bruce, D., Lucas, D., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision. In: Proc. DARPA Image Understanding Workshop, pp. 121–300 (1981)
Erdem, C.E., Tekalp, A.M., Sankur, B.: Non-Rigid Object Tracking Using Performance Evaluation Measures as Feedback. In: Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition, pp. 323–330 (2001)
Shi, J., Tomasi, C.: Good Features to Track. In: Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition, pp. 593–600 (1994)
Koschan, A., Kang, S., Paik, J., Abidi, B., Abidi, M.: Color Active Shape Models for Tracking Non-rigid Objects. Pattern Recognition Letters, 1751–1765 (2003)
Gharavi, H., Mills, M.: Block-Matching Motion Estimation Algorithms: New Results. IEEE Trans. Circ. and Syst., 649–651 (1990)
Goodall, C.: Procrustes Method in The Statistical Analysis of Shape. Journal of The Royal Statistical Society B, 285–339 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, S. et al. (2004). Optical Flow-Based Tracking of Deformable Objects Using a Non-prior Training Active Feature Model. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds) Advances in Multimedia Information Processing - PCM 2004. PCM 2004. Lecture Notes in Computer Science, vol 3333. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30543-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-540-30543-9_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23985-7
Online ISBN: 978-3-540-30543-9
eBook Packages: Computer ScienceComputer Science (R0)