Abstract
In this paper, a new framework is presented for the quantitative evaluation of the performance of appearance models composed of an object descriptor and a similarity measure in the context of object tracking. The evaluation is based on natural videos, and takes advantage of existing ground-truths from object tracking benchmarks. The proposed metrics evaluate the ability of an appearance model to discriminate an object from the clutter. This allows comparing models which may use diverse kinds of descriptors or similarity measures in a principled manner. The performances measures can be global, but time-oriented performance evaluation is also presented. The insights that the proposed framework can bring on appearance models properties with respect to tracking are illustrated on natural video data.
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References
Doermann, D., Mihalcik, D.: Tools and techniques for video performance evaluation. In: International Conference on Pattern Recognition. Barcelona, vol. 4, pp. 4167–4170 (2000)
Jaynes, C., Webb, S., Steele, R.M., Xiong, Q.: An open development environment for evaluation of video surveillance systems. In: International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), pp. 32–39 (2002)
CAVIAR: EU funded project, IST 2001 37540 (2004), http://homepages.inf.ed.ac.uk/rbf/CAVIAR/
Schneiders, S., Loos, T.J.H., Niem, W.: Performance evaluation of a real time video surveillance systems. In: International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS), pp. 15–16 (2005)
Brown, L., Senior, A., Tian, Y., Connell, J., Hampapur, A., Shu, C., Merhl, H., Lu, M.: Performance evaluation of surveillance systems under varying conditions. In: International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), Colorado (2005)
Bashir, F., Porikli, F.: Performance evaluation of object detection and tracking systems. In: International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), pp. 7–14 (2006)
Schlogl, T., Beleznai, C., Winter, M., Bischof, H.: Performance evaluation metrics for motion detection and tracking. In: International Conference on Pattern Recognition, vol. 4, pp. 519–522 (2004)
Smeulders, A., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval: The end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)
Deselaers, T., Keysers, D., Ney, H.: Features for image retrieval: A quantitative comparison. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds.) Pattern Recognition. LNCS, vol. 3175, pp. 228–236. Springer, Heidelberg (2004)
Muller, H., Muller, W., Squire, D.M., Marchand-Maillet, S., Pun, T.: Performance evaluation in content-based image retrieval: Overview and proposals. Pattern Recognition Letters 22(5), 593–601 (2001)
Geusebroek, J., Burghouts, G., Smeulders, A.: The Amsterdam library of object images. International Journal of Computer Vision 61(1), 103–112 (2005)
Smith, K., Ba, S., Odobez, J., Gatica-Perez, D.: Evaluating multi-object tracking. In: EEMCV. CVPR Workshop on Empirical Evaluation Methods in Computer Vision, San Diego, CA (2005)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(5), 564–575 (2003)
Black, J., Elis, T., Rosin, P.: A novel method for video tracking performance evaluation. In: International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), pp. 125–132 (2003)
Cutler, R., Davis, L.S.: Robust real-time periodic motion detection, analysis, and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 781–796 (2000)
Birchfield, S.T., Rangarajan, S.: Spatiograms versus histograms for region-based tracking. In: CVPR. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1158–1163. IEEE Computer Society Press, Los Alamitos (2005)
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Mikram, M., Mégret, R., Berthoumieu, Y. (2007). Evaluating Descriptors Performances for Object Tracking on Natural Video Data. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2007. Lecture Notes in Computer Science, vol 4678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74607-2_32
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DOI: https://doi.org/10.1007/978-3-540-74607-2_32
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