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Abstract
Object tracking quality usually depends on video context (e.g. object occlusion level, object density). In order to decrease this dependency, this paper presents a learning approach to adapt the tracker parameters to the context variations. In an offline phase, satisfactory tracking parameters are learned for video context clusters. In the online control phase, once a context change is detected, the tracking parameters are tuned using the learned values. The experimental results show that the proposed approach outperforms the recent trackers in state of the art. This paper brings two contributions: (1) a classification method of video sequences to learn offline tracking parameters, (2) a new method to tune online tracking parameters using tracking context.
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References
Kuo, C.H., Huang, C., Nevatia, R.: Multi-target tracking by online learned discriminative appearance models. In: CVPR (2010)
Borji, A., Frintrop, S., Sihite, D.N., Itti, L.: Adaptive Object Tracking by Learning Background Context. In: CVPR (2012)
Santner, J., Leistner, C., Saffari, A., Pock, T., Bischof, H.: PROST: Parallel Robust Online Simple Tracking. In: CVPR (2010)
Yoon, J.H., Kim, D.Y., Yoon, K.-J.: Visual Tracking via Adaptive Tracker Selection with Multiple Features. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 28–41. Springer, Heidelberg (2012)
Chau, D.P., Bremond, F., Thonnat, M.: A multi-feature tracking algorithm enabling adaptation to context variations. In: ICDP (2011)
Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Background modeling and subtraction by codebook construction. In: ICIP (2004)
Corvee, E., Bremond, F.: Body parts detection for people tracking using trees of Histogram of Oriented Gradient descriptors. In: AVSS (2010)
Bernardin, K., Stiefelhagen, R.: Evaluating Multiple Object Tracking Performance: The CLEAR MOTMetrics. EURASIP J. on Img. and Video Processing (2008)
Xing, J., Ai, H., Lao, S.: Multi-object tracking through occlusions by local tracklets filtering and global tracklets association with detection responses. In: CVPR (2009)
Li, Y., Huang, C., Nevatia, R.: Learning to Associate: HybridBoosted Multi-Target Tracker for Crowded Scene. In: CVPR (2009)
Berclaz, J., Fleuret, F., Turetken, E., Fua, P.: Multiple object tracking using k-shortest paths optimization. TPAMI 33, 1806–1819 (2011)
Shitrit, J., Berclaz, J., Fleuret, F., Fua, P.: Tracking multiple people under global appearance constraints. In: ICCV (2011)
Henriques, J.F., Caseiro, R., Batista, J.: Globally optimal solution to multi-object tracking with merged measurements. In: ICCV (2011)
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Authors and Affiliations
STARS team, INRIA Sophia Antipolis, France
Duc Phu Chau, Monique Thonnat & François Brémond
- Duc Phu Chau
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- Monique Thonnat
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- François Brémond
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Editors and Affiliations
Intel Science and Technology Center on Embedded Computing, Pittsburgh, PA, USA
Mei Chen
UMIC Research Centre, RWTH Aachen University, Aachen, Germany
Bastian Leibe
FB Informatik, Universität Hamburg, Germany
Bernd Neumann
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Chau, D.P., Thonnat, M., Brémond, F. (2013). Automatic Parameter Adaptation for Multi-object Tracking. In: Chen, M., Leibe, B., Neumann, B. (eds) Computer Vision Systems. ICVS 2013. Lecture Notes in Computer Science, vol 7963. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39402-7_25
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