Abstract
The need for empirical evaluation metrics and algorithms is well acknowledged in the field of computer vision. The process leads to precise insights to understanding current technological capabilities and also helps in measuring progress. Hence designing good and meaningful performance measures is very critical.
In this paper, we propose two comprehensive measures, one each for detection and tracking, for video domains where an object bounding approach to ground truthing can be followed. Thorough analysis explaining the behavior of the measures for different types of detection and tracking errors are discussed. Face detection and tracking is chosen as a prototype task where such an evaluation is relevant. Results on real data comparing existing algorithms are presented and the measures are shown to be effective in capturing the accuracy of the detection/tracking systems.
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References
Antani, S., Crandall, D., Narasimhamurthy, A., Mariano, V.Y., Kasturi, R.: Evaluation of Methods for Detection and Localization of Text in Video. In: Proceedings in International Workshop on Document Analysis Systems (2000)
Black, J., Ellis, T.J., Rosin, P.: A Novel Method for Video Tracking Performance Evaluation. In: Proceedings of IEEE PETS Workshop (2003)
Brown, L.M., Senior, A.W., Tian, Y., Connell, J., Hampapur, A., Shu, C., Merkl, H., Lu, M.: Performance Evaluation of Surveillance Systems Under Varying Conditions. In: Proceedings of IEEE PETS Workshop (2005)
Collins, R., Zhou, X., Teh, S.: An Open Source Tracking Testbed and Evaluation Web Site. In: Proceedings of IEEE PETS Workshop (2005)
Fisher, R.B.: The PETS04 Surveillance Ground-Truth Data Sets. In: Proceedings of IEEE PETS Workshop (2004)
Hua, X., Wenyin, L., Zhang, H.: Automatic Performance Evaluation for Video Text Detection. In: Proc. International Conference on Document Analysis and Recognition (2001)
Nascimento, J., Marques, J.: New Performance Evaluation Metrics for Object Detection Algorithms. In: Proceedings of IEEE PETS Workshop (2004)
Smith, K., Gatica-Perez, D., Odobez, J., Ba, S.: Evaluating Multi-Object Tracking. In: Proceedings of IEEE Empirical Evaluation Methods in Computer Vision Workshop (2005)
Doermann, D., Mihalcik, D.: Tools and Techniques for Video Performance Evaluation. In: ICPR, vol. 4, pp. 167–170 (2000)
Munkres, J.R.: Algorithms for the Assignment and Transportation Problems. J. SIAM 5, 32–38 (1957)
Fredman, M.L., Tarjan, R.E.: Fibonacci Heaps and their uses in Improved Network Optimization Algorithms. Journal of ACM 34, 596–615 (1987)
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© 2006 Springer-Verlag Berlin Heidelberg
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Manohar, V., Soundararajan, P., Raju, H., Goldgof, D., Kasturi, R., Garofolo, J. (2006). Performance Evaluation of Object Detection and Tracking in Video. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3852. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612704_16
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DOI: https://doi.org/10.1007/11612704_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31244-4
Online ISBN: 978-3-540-32432-4
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