Abstract
While the analysis of foreground silhouettes has become a key component of modern approach to multi-view people detection, it remains subject to errors when dealing with a single viewpoint. Besides, several works have demonstrated the benefit of exploiting classifiers to detect objects or people in images, based on local texture statistics. In this paper, we train a classifier to differentiate false and true positives among the detections computed based on a foreground mask analysis. This is done in a sport analysis context where people deformations are important, which makes it important to adapt the classifier to the case at hand, so as to take the teamsport color and the background appearance into account. To circumvent the manual annotation burden incurred by the repetition of the training for each event, we propose to train the classifier based on the foreground detector decisions. Hence, since the detector is not perfect, we face a training set whose labels might be corrupted. We investigate a set of classifier design strategies, and demonstrate the effectiveness of the approach to reliably detect sport players with a single view.
Part of this work has been funded by the Belgian NSF, and the walloon region project SPORTIC.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02895-8_64
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alahi, A., Jacques, L., Boursier, Y., Vandergheynst, P.: Sparsity driven people localization with a heterogeneous network of cameras. Jour. of MIV 41(1-2), 39–58 (2011)
Amit, Y., Geman, D.: Shape quantization and recognition with randomized trees. Neural Computation 9(12), 1545–1588 (1997)
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proc. of COLT, pp. 92–100 (1998)
Bosch, A., Zisserman, A., Munoz, X.: Image classification using random forests and ferns. In: Proc. of ICCV (2007)
Chen, F., Delannay, D., De Vleeschouwer, C.: An autonomous framework to produce and distribute personalized team-sport video summaries: a basket-ball case study. IEEE Trans. on Multimedia 13(6), 1381–1394 (2011)
Delannay, D., Danhier, N., De Vleeschouwer, C.: Detection and recognition of sports (wo)men from multiple views. In: Proc. of ACM/IEEE ICDSC (2009)
Dollar, P., Tu, Z., Perona, P., Belongie, S.: Integral channel features. In: Proc. of BMVC (2009)
Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: a benchmark. In: Proc. of IEEE CVPR (2009)
Felzenszwalb, P., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. on PAMI 32(9), 1627–1645 (2010)
Fleuret, F., Berclaz, J., Lengagne, R., Fua, P.: Multi-camera people tracking with a probabilistic occupancy map. IEEE Trans. on PAMI 30(2), 267–282 (2008)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Jour. of CSS 55(1), 119–139 (1997)
Geurts, P., Ernst, D., Wehenkel, L.: Extremely Randomized Trees. Machine Learning 36(1), 3–42 (2006)
Khan, S.M., Shah, M.: A multiview approach to tracking people in crowded scenes using a planar homography constraint. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 133–146. Springer, Heidelberg (2006)
Levin, A., Viola, P., Freund, Y.: Unsupervised improvement of visual detectors using co-training. In: ICCV, pp. 626–633 (2003)
Marée, R., Geurts, P., Piater, J., Wehenkel, L.: Random subwindows for robust image classification. In: Proc. of IEEE CVPR, pp. 34–40 (2005)
Nair, V., Clark, J.J.: An unsupervised, online learning framework for moving object detection. In: Proc. of IEEE CVPR, vol. 2, pp. 317–324 (2004)
Ozuysal, M., Calonder, M., Lepetit, V., Fua, P.: Fast keypoint recognition using random ferns. IEEE Trans. on PAMI 32(3), 448–461 (2010)
Roth, P., Grabner, H., Skočaj, D., Bischof, H., Leonardis, A.: Conservative visual learning for object detection with minimal hand labeling effort. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 293–300. Springer, Heidelberg (2005)
Viola, P., Jones, M.: Robust real-time object detection. In: Proc. of the Int. Workshop on SCTV (2001)
Xing, J., Ai, H., Liu, L., Lao, S.: Multiple player tracking in sports video: A dual-mode two-way bayesian inference approach with progressive observation modeling. IEEE Trans. on Image Processing 20(6), 1652–1667 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Parisot, P., Sevilmiş, B., De Vleeschouwer, C. (2013). Training with Corrupted Labels to Reinforce a Probably Correct Teamsport Player Detector. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2013. Lecture Notes in Computer Science, vol 8192. Springer, Cham. https://doi.org/10.1007/978-3-319-02895-8_61
Download citation
DOI: https://doi.org/10.1007/978-3-319-02895-8_61
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02894-1
Online ISBN: 978-3-319-02895-8
eBook Packages: Computer ScienceComputer Science (R0)