Abstract
This paper proposes an online tracking method which has been inspired by studying the effects of Scale Invariant Feature Transform (SIFT) when applied to objects assumed to be flat even though they are not. The consequent deviation from flatness induces nuisance factors that act on the feature representation in a manner for which no general local invariants can be computed, such as in the case of occlusion, sensor quantization and casting shadows. However, if features are over-represented, they can provide the necessary information to build online, a robust object/context discriminative classifier. This is achieved based on weakly aligned multiple instance local features in a sense that will be made clear in the rest of this paper. According to this observation, we present a non parametric online tracking by detection approach that yields state of the art performance.
Specific tests on video sequences of faces show excellent long-term tracking performance in unconstrained videos.
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Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Computing Surveys 38, 13 (2006)
Lepetit, V., Fua, P.: Monocular model-based 3d tracking of rigid objects. Found. Trends. Comput. Graph. Vis. 1, 1–89 (2005)
Ross, D.A., Lim, J., Lin, R.S., Yang, M.H.: Incremental learning for robust visual tracking. Int. J. Comput. Vision 77, 125–141 (2008)
Yu, Q., Dinh, T.B., Medioni, G.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)
Mei, X., Ling, H.: Robust visual tracking using l1 minimization. In: ICCV 2009, pp. 1436–1443 (2009)
Santner, J., Leistner, C., Saffari, A., Pock, T., Bischof, H.: Prost: Parallel robust online simple tracking. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 723–730 (2010)
Kalal, Z., Matas, J., Mikolajczyk, K.: P-n learning: Bootstrapping binary classifiers by structural constraints. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2010)
Babenko, B., Yang, M.H., Belongie, S.: Robust object tracking with online multiple instance learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 1619–1632 (2011)
Dinh, T.B., Vo, N., Medioni, G.: Context tracker: Exploring supporters and distracters in unconstrained environments. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2011)
Wang, Q., Chen, F., Xu, W., Yang, M.H.: An experimental comparison of online object tracking algorithms. In: Proceedings of SPIE: Image and Signal Processing Track (2011)
Matthews, I., Ishikawa, T., Baker, S.: The template update problem. In: Proceedings of the British Machine Vision Conference (2003)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Grabner, H., Bischof, H.: On-line boosting and vision. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 260–267 (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)
Stalder, S., Grabner, H., Van Gool, L.: Beyond semi-supervised tracking: Tracking should be as simple as detection, but not simpler than recognition. In: OLCV 2009: 3rd On-line learning for Computer Vision Workshop (2009)
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Pernici, F. (2012). FaceHugger: The ALIEN Tracker Applied to Faces. In: Fusiello, A., Murino, V., Cucchiara, R. (eds) Computer Vision – ECCV 2012. Workshops and Demonstrations. ECCV 2012. Lecture Notes in Computer Science, vol 7585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33885-4_61
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DOI: https://doi.org/10.1007/978-3-642-33885-4_61
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