Abstract
We present a novel analysis of the state of the art in object tracking with respect to diversity found in its main component, an ensemble classifier that is updated in an online manner. We employ established measures for diversity and performance from the rich literature on ensemble classification and online learning, and present a detailed evaluation of diversity and performance on benchmark sequences in order to gain an insight into how the tracking performance can be improved.
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References
Babenko, B., Yang, M.H., Belongie, S.: Robust object tracking with online multiple instance learning. Pattern Analysis and Machine Intelligence 33(8) (August 2011)
Bertolami, R., Bunke, H.: Diversity analysis for ensembles of word sequence recognisers. In: Yeung, D.-Y., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds.) SSPR&SPR 2006. LNCS, vol. 4109, pp. 677–686. Springer, Heidelberg (2006)
Breiman, L.: Bagging Predictors. Machine Learning 24, 123–140 (1996)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Brown, G., Kuncheva, L.I.: “Good” and “Bad” Diversity in Majority Vote Ensembles. In: El Gayar, N., Kittler, J., Roli, F. (eds.) MCS 2010. LNCS, vol. 5997, pp. 124–133. Springer, Heidelberg (2010)
Brown, G., Wyatt, J., Harris, R., Yao, X.: Diversity creation methods: A survey and categorisation. Journal of Information Fusion 6, 5–20 (2005)
Chen, H., Yao, X.: Multiobjective neural network ensembles based on regularized negative correlation learning. Knowledge and Data Engineering 22(12) (2010)
Collins, R.T., Liu, Y., Leordeanu, M.: Online selection of discriminative tracking features. Pattern Analysis and Machine Intelligence 27(10), 1631–1643 (2005)
Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)
Dietterich, T.G., Bakiri, G.: Solving Multiclass Learning Problems via Error-Correcting Output Codes. Journal of Artificial Intelligence Research 2 (1995)
Everingham, M., Van Gool, L., Williams, C., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. International Journal of Computer Vision 88(2), 303–338 (2010)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Vitányi, P.M.B. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995)
Frinken, V., Peter, T., Fischer, A., Bunke, H., Do, T.-M.-T., Artieres, T.: Improved handwriting recognition by combining two forms of hidden markov models and a recurrent neural network. In: Jiang, X., Petkov, N. (eds.) CAIP 2009. LNCS, vol. 5702, pp. 189–196. Springer, Heidelberg (2009)
Grabner, H., Bischof, H.: On-line boosting and vision. In: Computer Vision and Pattern Recognition, vol. 1 (2006)
Grabner, H., Leistner, C., Bischof, H.: Semi-supervised On-Line boosting for robust tracking. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 234–247. Springer, Heidelberg (2008)
Ho, T.K.: The random subspace method for constructing decision forests. Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)
Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-Learning-detection. Pattern Analysis and Machine Intelligence 34(7), 1409–1422 (2012)
Kuncheva, L.I., Whitaker, C.J.: Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy. Machine Learning 51(2), 181–207 (2003)
Levy, N., Wolf, L.: Minimal correlation classification. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 29–42. Springer, Heidelberg (2012)
Liu, Y., Yao, X., Higuchi, T.: Evolutionary ensembles with negative correlation learning. Evolutionary Computation 4(4), 380–387 (2000)
Minku, L.L., White, A.P., Yao, X.: The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift. Knowledge and Data Engineering 22(5), 730–742 (2010)
Oza, N.C.: Online Bagging and Boosting. Systems, Man and Cybernetics (2005)
Ozuysal, M., Calonder, M., Lepetit, V., Fua, P.: Fast keypoint recognition using random ferns. Pattern Analysis and Machine Intelligence 32(3), 448–461 (2010)
Saffari, A., Leistner, C., Santner, J., Godec, M., Bischof, H.: On-line random forests. In: International Conference on Computer Vision Workshops (2009)
Santner, J., Leistner, C., Saffari, A., Pock, T., Bischof, H.: PROST: Parallel robust online simple tracking. In: Computer Vision and Pattern Recognition (2010)
Visentini, I., Kittler, J., Foresti, G.L.: Diversity-based classifier selection for adaptive object tracking. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds.) MCS 2009. LNCS, vol. 5519, pp. 438–447. Springer, Heidelberg (2009)
Yu, Q., Dinh, T.B., Medioni, G.: Online tracking and reacquisition using co-trained generative and discriminative trackers. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 678–691. Springer, Heidelberg (2008)
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Nebehay, G., Chibamu, W., Lewis, P.R., Chandra, A., Pflugfelder, R., Yao, X. (2013). Can Diversity amongst Learners Improve Online Object Tracking?. In: Zhou, ZH., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2013. Lecture Notes in Computer Science, vol 7872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38067-9_19
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DOI: https://doi.org/10.1007/978-3-642-38067-9_19
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