Abstract
Active Shape Model (ASM) paradigm is a popular method for image segmentation where a priori information about the shape of the object of interest is available. The effectiveness of the method is contingent upon a correct correspondence between model points and the features extracted from the image. Extensive application of these models soon revealed one of their limitations when, for a given model point, no obvious salient point can be found in the image. The primary cause of such limitation is due to weak edges and presence of abrupt noise which is the case with low light surveillance video images. In this paper we propose a fusion-based panoramic tracking algorithm of in low light images using multiple sensors. The proposed algorithm uses an IR and CCD sensor for image capture. The proposed tracking system consists of three steps: (i) pyramid based fusion algorithm, (ii) reconstruction of panoramic image, and (iii) active shape model (ASM)-based tracking algorithm. The experimental results show that the proposed tracking system can robustly extract and track objects on panoramic images in real-time.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Haritaoglu, I., Harwood, D., Davis, L.S.: W4: Real-time surveillance of people and their activities. IEEE Trans. Pattern Analysis, Machine Intelligence 22(8), 809–830 (2000)
Koschan, A., Kang, S.K., Paik, J.K., Abidi, B.R., Abidi, M.A.: Video object tracking based on extended active shape models with color information. In: Proc. 1st European Conf. Color in Graphics, Imaging, Vision, April 2002, pp. 126–131. University of Poitiers, France (2002)
Chang, P., Krumm, J.: Object recognition with color occurrence histograms. In: IEEE Conf. on Computer Vision and Pattern Recognition, Fort Collins, CO (June 1999)
Horprasert, T., Harwood, D., Davis, L.S.: A robust background subtraction and shadow detection. In: Proc. ACCV 2000, Taipie, Taiwan (January 2000)
Chen, C., Hsieh, W., Chen, J.: Panoramic appearance-based recognition of video contents using matching graphs. IEEE Transactions on Systems, Man, and Cybernetics-PART B: Cybernetics 34(1) (February 2004)
Kim, S., Kang, J., Shin, J., Lee, S., Paik, J., Kang, S., Abidi, B., Abidi, M.: Optical flow-based tracking of deformable object using a non-prior training active feature model. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds.) PCM 2004. LNCS, vol. 3333, pp. 69–78. Springer, Heidelberg (2004)
Zhu, Z., Hanson, A., Riseman, E.: Generalized parallel-perspective stereo mosaics from airborne video. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(2), 226–237 (2004)
Patil, R., Rybski, P., Kanade, T., Veloso, M.: People detection and tracking in high resolution panoramic video mosaic. In: Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, September 2004, pp. 1323–1328 (2004)
Wren, C.R., Azerbayejani, A., Darrell, T., Pentland, A.P.: Pfinder: Real-time tracking of the humand body. IEEE Trans. Pattern Anal. Machine Intell. 19, 780–785 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, D., Maik, V., Lee, D., Shin, J., Paik, J. (2006). Active Shape Model-Based Object Tracking in Panoramic Video. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science – ICCS 2006. ICCS 2006. Lecture Notes in Computer Science, vol 3994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11758549_123
Download citation
DOI: https://doi.org/10.1007/11758549_123
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34385-1
Online ISBN: 978-3-540-34386-8
eBook Packages: Computer ScienceComputer Science (R0)