Abstract
We present the results of a study to exploit a multiple colour space model (CSM) and variable kernels for object tracking in video sequences. The basis of our work is the mean shift algorithm; for a moving target, we develop a procedure to adaptively change the CSM throughout a video sequence. The optional CSM components are ranked using a similarity distance within an inner (representing the object) and outer (representing the surrounding region) rectangle. Rather than use the standard, Epanechnikov kernel, we have also used a kernel weighted by the normalized Chamfer distance transform to improve the accuracy of target representation and localization, minimising the distance between the two distributions of foreground and background using the Bhattacharya coefficient. To define the target shape in the rectangular window, either regional segmentation or background-difference imaging, dependent on the nature of the video sequence, has been used. Experimental results show the improved tracking capability and versatility of the algorithm in comparison with results using fixed colour models and standard kernels.
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
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(5), 564–575 (2003)
McKenna, S.J., Raja, Y., Gong, S.: Tracking colour objects using adaptive mixture models. Image and Vision Computing 17, 225–231 (1999)
Chen, Y., Rui, Y., Huang, T.: JPDAF-based HMM for real-time contour tracking. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, vol. I, pp. 543–550. IEEE Computer Society Press, Los Alamitos (2001)
Geiger, D., Gupta, A., Costa, L.A., Vlontzos, J.: Dynamic programming for detecting, tracking, and matching deformable contours. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(3), 294–302 (1995)
Hsu, R.L., Abdel-Mottaleb, M., Jain, A.K.: Face Detection in Color Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 696–706 (2002)
Zarit, B.D., Super, B.J., Quek, F.K.H.: Comparison of five color models in skin pixel classification. In: Proc. on International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, pp. 58–63 (1999)
Sobottka, K., Pitas, I.: Segmentation and tracking of faces in color images. In: Proceedings of the Second International Conference on Automatic Face and Gesture Recognition, October 1996, pp. 236–241 (1996)
Kumar, R., Sawhney, H., et al.: Aerial video surveillance and exploitation. Proceedings of the IEEE 89(10), 1518–1539 (2001)
Cohen, I., Medioni, G.: Detecting and tracking moving objects in video surveillance. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 319–325 (1999)
Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 603–619 (2002)
Singh, M., Ahuja, N.: Regression based bandwidth selection for segmentation using Parzen windows. In: ICCV 2003. Proceedings of International Conference of Computer Vision, vol. 1, pp. 2–9 (2003)
Chen, H., Meer, P.: Robust computer vision through kernel density estimation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 236–250. Springer, Heidelberg (2002)
Collins, R.T., Liu, Y., Leordeanu, M.: Online selection of discriminative tracking features. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(10), 1631–1643 (2005)
Bhattacharyya, A.: On a measure of divergence between two statistical populations defined by their probability distributions. Bulletin of the Calcutta Mathematics Society 35, 99–110 (1943)
Chen, Z., Husz, Z.L., Wallace, I., Wallace, A.M.: Video object tracking based on a Chamfer distance transform. In: Proceedings of IEEE International Conference on Image Processing, San Antonio, Texas, September 2007, IEEE Computer Society Press, Los Alamitos (2007)
Kailath, T.: The divergence and Bhattacharyya distance measures in signal selection. IEEE Transactions on Communication Technology 15(1), 52–60 (1967)
Stern, H., Efros, B.: Adaptive color space swiching for face tracking in multi-coloredlighting environments. In: Fifth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 236–241. IEEE Computer Society Press, Los Alamitos (2002)
Quek, F., Mysliwiec, T.A., Zhao, M.: FingerMouse: A Freehand Computer Pointing Interface. In: Proc. of Int’l Conf. on Automatic Face and Gesture Recognition, Zurich, Switzerland, pp. 372–377 (1995)
Yang, J., Lu, W., Waibel, A.: Skin-color modeling and adaptation. In: Proceedings of the 3rd Asian Conference on Computer Vision, vol. 2, pp. 687–694 (1998)
Liu, Y., Schmidt, K., Cohn, J., Mitra, S.: Facial asymmetr quantification for expression invariant human identification. Computer Vision and Image Understanding 91(1/2), 138–159 (2003)
Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. International Journal of Computer Vision 40(2), 99–121 (2000)
Stricker, M., Orengo, M.: Similarity of color images. In: SPIE Conference on Storage and Retrieval for Image and Video Databases III, vol. 2420, pp. 381–392 (1995)
Cheng, Y.Z.: Mean shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(8), 790–799 (1995)
Borgefors, G.: Hierarchical chamfer matching: A parametric edge matching algorithm. IEEE Transaction on Pattern Analysis and Machine Intelligence 10(6), 849–865 (1988)
Porikli, F.M., Tuzel, O.: Human body tracking by adaptive background models and mean-shift analysis. In: IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, Z., Wallace, A.M. (2007). Improved Object Tracking Using an Adaptive Colour Model. In: Yuille, A.L., Zhu, SC., Cremers, D., Wang, Y. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2007. Lecture Notes in Computer Science, vol 4679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74198-5_22
Download citation
DOI: https://doi.org/10.1007/978-3-540-74198-5_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74195-4
Online ISBN: 978-3-540-74198-5
eBook Packages: Computer ScienceComputer Science (R0)