Abstract
A successful approach for object tracking has been kernel based object tracking [1] by Comaniciu et al.. The method provides an effective solution to the problems of representation and localization in tracking. The method involves representation of an object by a feature histogram with an isotropic kernel and performing a gradient based mean shift optimization for localizing the kernel. Though robust, this technique fails under cases of occlusion. We improve the kernel based object tracking by performing the localization using a generalized (bidirectional) mean shift based optimization. This makes the method resilient to occlusions. Another aspect related to the localization step is handling of scale changes by varying the bandwidth of the kernel. Here, we suggest a technique based on SIFT features [2] by Lowe to enable change of bandwidth of the kernel even in the presence of occlusion. We demonstrate the effectiveness of the techniques proposed through extensive experimentation on a number of challenging data sets.
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References
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 564–575 (2003)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 603–619 (2002)
Babu, V., Perez, P., Bouthemy, P.: Kernel-based robust tracking for objects. In: Proc. Asian Conference on Computer Vision, Hyderabad India, Part II, pp. 353–362 (2006)
Isard, M., MacCormick, J.: Bramble: A bayesian multiple-blob tracker. In: Proc.IEEE International Conf. on Computer Vision (ICCV), vol. 2, pp. 34–41 (2001)
Comaniciu, D., Ramesh, V., Meer, P.: The variable bandwidth mean shift and data-driven scale selection. In: Proceedings of IEEE International Conference on Computer Vision, Vancouver, Canada, vol. 1, pp. 438–445 (2001)
Comaniciu, D.: An algorithm for data-driven bandwidth selection. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 281–288 (2003)
Collins, R.T.: Mean shift blob tracking through scale space. In: CVPR 2003 Conference Proceedings, Madison, Wisconsin, June 2003, pp. 234–240 (2003)
Fukunaga, K., Hostetler, L.D.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory 21, 32–40 (1975)
Namboodiri, V.P., Chaudhuri, S.: Shock filters based on implicit cluster separation. In: Proc. Conference on Computer Vision and Pattern Recognition (CVPR 2005), San Diego, CA, USA, June 20-26, 2005, pp. 82–87 (2005)
Fisher, R.B.: Pets04 surveillance ground truth data set. In: Proc. Sixth IEEE Int. Work. on Performance Evaluation of Tracking and Surveillance, pp. 1–5 (2004)
Jojic, N., Frey, B.: Learning flexible sprites in video layers. In: Proc.IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 199–206 (2001)
Haag, M., Nagel, H.H.: Tracking of complex driving manoeuvres in traffic image sequences. Image and Vision Computing 16, 517–527 (1998)
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© 2006 Springer-Verlag Berlin Heidelberg
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Namboodiri, V.P., Ghorawat, A., Chaudhuri, S. (2006). Improved Kernel-Based Object Tracking Under Occluded Scenarios. In: Kalra, P.K., Peleg, S. (eds) Computer Vision, Graphics and Image Processing. Lecture Notes in Computer Science, vol 4338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949619_45
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DOI: https://doi.org/10.1007/11949619_45
Publisher Name: Springer, Berlin, Heidelberg
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