Abstract
This paper demonstrates how a simple, yet effective, set of features enables to integrate ensemble classifiers in optical flow based tracking. In particular, gray value differences of pixel pairs are used for generating binary weak classifiers, forming the respective object representation. For the tracking step an affine motion model is proposed. By using hinge loss functions, the motion estimation problem can be formulated as a linear program. Experiments demonstrate robustness of the proposed approach and include comparisons to conventional tracking methods.
This work has been sponsored by the Austrian Joint Research Project Cognitive Vision under projects S9103-N04 and S9104-N04.
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References
Avidan, S.: Support vector tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(8), 1064–1072 (2004)
Avidan, S.: Ensemble tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(2), 261–271 (2007)
Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)
Dietterich, T.G.: Ensemble methods in machine learning. In: Proceedings International Workshop on Multiple Classifier Systems, pp. 1–15 (2000)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)
Grabner, H., Bischof, H.: On-line boosting and vision. In: Proceedings Conference Computer Vision and Pattern Recognition, vol. 1, pp. 260–267 (2006)
Grabner, H., Grabner, M., Bischof, H.: Real-time tracking via on-line boosting. In: Proceedings British Machine Vision Conference, vol. 1, pp. 47–56 (2006)
Jurie, F., Dhome, M.: A simple and efficient template matching algorithm. In: Proceedings International Conference on Computer Vision, vol. 2, pp. 544–549 (2001)
Kolter, J.Z., Maloof, M.A.: Dynamic weighted majority: A new ensemble method for tracking concept drift. In: Proceedings International Conference on Data Mining, pp. 123–130 (2003)
Lepetit, V.: Keypoint recognition using randomized trees. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(9), 1465–1479 (2006)
Littlestone, N., Warmuth, M.K.: The weighted majority algorithm. Information and Computation 108(2), 212–261 (1994)
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings International Joint Conference on Artificial Intelligence, pp. 674–679 (1981)
Matas, J., Hilton, A., Zimmermann, K., Svoboda, T.: Learning efficient linear predictors for motion estimation. In: Indien Conference on Computer Vision, Graphics and Image Processing, pp. 445–456 (2006)
Özuysal, M., Lepetit, V., Fleuret, F., Fua, P.: Feature harvesting for tracking-by-detection. In: Proceedings European Conference on Computer Vision, vol. 3, pp. 592–605 (2006)
Pele, O., Werman, M.: Robust real time pattern matching using bayesian sequential hypothesis testing. IEEE Transaction Pattern Analysis and Machine Intelligence (to appear, 2008)
Tian, M., Zhang, W., Liu, F.: On-line ensemble svm for robust object tracking. In: Proceedings Asian Conference on Computer Vision, pp. 355–364 (2007)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings Conference Computer Vision and Pattern Recognition, vol. I, pp. 511–518 (2001)
Williams, O., Blake, A., Cipolla, R.: Sparse bayesian learning for efficient visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1292–1304 (2005)
Zabih, R., Woodfill, J.: Non-parametric local transforms for computing visual correspondence. In: Proceedings European Conference on Computer Vision, vol. 2, pp. 151–158 (1994)
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Grabner, M., Zach, C., Bischof, H. (2008). Efficient Tracking as Linear Program on Weak Binary Classifiers. In: Rigoll, G. (eds) Pattern Recognition. DAGM 2008. Lecture Notes in Computer Science, vol 5096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69321-5_11
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DOI: https://doi.org/10.1007/978-3-540-69321-5_11
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