Abstract
This paper presents an approach for evaluating multiple color histograms during object tracking. The method adaptively selects histograms that well distinguish foreground from background. The variance ratio is utilized to measure the separability of object and background and to extract top-ranked discriminative histograms. Experimental results demonstrate how this method adapts to changing appearances of both object undergoing tracking and surrounding background. The advantages and limitations of the particle filter with embedded mechanism of histogram selection are demonstrated in comparisons with the standard CamShift tracker and a combination of CamShift with histogram selection.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, London (1990)
Collins, R., Liu, Y.: On-line selection of discriminative tracking features. In: Proc. of the Int. Conf. on Computer Vision, Nice, France, pp. 346–352 (2003)
Han, B., Davis, L.: Object tracking by adaptive feature extraction. In: Proc. of Int. Conf. on Image Processing (ICIP), Singapore, vol. III, pp. 1501–1504 (2004)
Shi, J., Tomasi, C.: Good features to track. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, Seattle, Washington, pp. 593–600 (1994)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons, Inc., Chichester (2001)
Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: Proc. of IEEE Conf. on Comp. Vision and Pattern Recognition, Hilton Head, SC, pp. 142–149 (2000)
Nguyen, H.T., Smeulders, A.: Tracking aspects of the foreground against the background. In: 8th European Conf. on Computer Vision, Prague, Czech Republic, pp. 446–456 (2004)
Wu, Y., Huang, T.S.: Color tracking by transductive learning. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, Hilton Head Island, South Carolina, pp. 133–138 (2000)
Stern, H., Efros, B.: Adaptive color space switching for tracking under varying illumination. Image and Vision Computing 23, 353–364 (2005)
Swain, M.J., Ballard, D.H.: Color indexing. Int. Journal of Computer Vision 7, 11–32 (1991)
Birchfield, S.: Elliptical head tracking using intensity gradients and color histograms. In: IEEE Conf. on Computer Vision and Pattern Recognition, Santa Barbara, CA, pp. 232–237 (1998)
Bradski, G.R.: Computer vision face tracking as a component of a perceptual user interface. In: Proc. IEEE Workshop on Applications of Comp. Vision, Princeton, pp. 214–219 (1998)
Perez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: 7th European Conf. on Computer Vision, Copenhagen, Denmark, pp. 661–675 (2002)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. of Computer Vision 60, 91–110 (2004)
Horn, B.K.P.: Robot Vision. The MIT Press, Cambridge (1986)
Isard, M., Blake, A.: Contour tracking by stochastic propagation of conditional density. In: 4th European Conf. on Computer Vision, Cambridge, UK, pp. 343–356 (1996)
Doucet, A., Godsill, S., Andrieu, C.: On sequential monte carlo sampling methods for bayesian filtering. Statistics and Computing 10, 197–208 (2000)
Han, B., Zhu, Y., Davis, L.: Incremental density approximation and kernel-based bayesian filtering for object tracking. In: Int. Conf. on Computer Vision and Pattern Recognition, Washington, DC, pp. 638–644 (2004)
Fritsch, J., Kwolek, B.: Kernel particle filter for real-time 3d body tracking in monocular color images. In: IEEE Int. Conf. on Face and Gesture Recognition, Southampton, UK, pp. 564–567. IEEE Computer Society Press, Los Alamitos (2006)
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
Kwolek, B. (2006). Object Tracking Using Discriminative Feature Selection. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2006. Lecture Notes in Computer Science, vol 4179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11864349_26
Download citation
DOI: https://doi.org/10.1007/11864349_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44630-9
Online ISBN: 978-3-540-44632-3
eBook Packages: Computer ScienceComputer Science (R0)