Abstract
In this work we propose a mechanism which looks at processing the low-level visual information present in video frames and prepares mid-level tracking trajectories of objects of interest within the video. The main component of the proposed framework takes detected objects as inputs and generates their appearance models, maintains them and tracks these individuals within the video. The proposed object tracking algorithm is also capable of detecting the possibility of collision between the object trajectories and resolving it without losing their models.
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
Greiffenhagen, M., Comaniciu, D., Neimann, H., Ramesh, V.: Design, analysis and engineering of video monitoring systems: An approach and a case study. Proceedings of the IEEE 89, 1498–1517 (2001)
Bradski, G.R.: Computer vision face tracking as a component of a perceptual user interface. In: IEEE Workshop on Applications of Computer Vision, pp. 214–219 (1998)
Handman, U., Kalinke, T., Tzomakas, C., Werner, M., von Seelen, W.: Computer vision for driver assistance systems. In: Proceedings of SPIE, vol. 3364, pp. 136–147 (1998)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25, 564–575 (2003)
Bar-Shalom, Y.: Tracking and data association. Academic Press Professional, Inc., San Diego (1987)
Kitagawa, G.: Non-gaussian state-space modeling of nonstationary time series. Journal of American Statistical Association 82, 1032–1063 (1987)
Gordon, G., Salmond, D., Smith, A.: A novel approach to non-linear and non-gaussian bayesian state estimation. Proceedings of IEEE 140, 107–113 (1993)
Rabiner, L.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of IEEE 77, 257–285 (1989)
Tuzel, O., Porikli, F., Meer, P.: Learning on lie groups for invariant detection and tracking, Mineapolis, MN, pp. 1–8 (2008)
Loza, A., Mihaylova, L., Bull, D., Canagarajah, N.: Structural similarity-based object tracking in multimodality surveillance videos. Mach. Vision Appl. 20, 71–83 (2009)
Zhou, H., Yuan, Y., Westover, C.S.: Object tracking using SIFT features and mean shift. Computer Vision and Image Understanding 3, 345–352 (2009)
Tavakkoli, A., Nicolescu, M., Bebis, G.: Efficient background modeling through incremental support vector data description. In: Proceedings of the 19th International Conference on Pattern Recognition (2008)
Broida, T., Chellappa, R.: Estimation of object motion parameters from noisy images, vol. 8, pp. 90–99 (1986)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tavakkoli, A., Nicolescu, M., Bebis, G. (2010). A Spatio-Spectral Algorithm for Robust and Scalable Object Tracking in Videos. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17277-9_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-17277-9_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17276-2
Online ISBN: 978-3-642-17277-9
eBook Packages: Computer ScienceComputer Science (R0)