Abstract
In this paper, we compare and evaluate five contemporary, data-driven, real-time 2D object tracking methods: the region tracker by Hager et al., the Hyperplane tracker, the CONDENSATION tracker, and the Mean Shift and Trust Region trackers. The first two are classical template based methods, while the latter three are from the more recently proposed class of histogram based trackers. All trackers are evaluated for the task of pure translation tracking, as well as tracking translation plus scaling. For the evaluation, we use a publically available, labeled data set consisiting of surveillance videos of humans in public spaces. This data set demonstrates occlusions, changes in object appearance, and scaling.
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References
Hager, G., Belhumeur, P.: Efficient region tracking with parametric models of geometry and illumination. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 1025–1039 (1998)
Jurie, F., Dhome, M.: Hyperplane approach for template matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 996–1000 (2002)
Comaniciu, D., Meer, P., Ramesh, V.: Kernel-Based Object Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 564–575 (2004)
Liu, T.L., Chen, H.T.: Real-Time Tracking Using Trust-Region Methods. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 397–402 (2004)
Perez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-Based Probabilistic Tracking. In: 7th European Conference on Computer Vision, vol. 1, pp. 661–675 (2002)
CAVIAR: EU funded project, IST 2001 37540 (2004), http://homepages.inf.ed.ac.uk/rbf/CAVIAR/
Comaniciu, D.: Bayesian Kernel Tracking. In: Annual Conference of the German Society for Pattern Recognition, pp. 438–445 (2002)
Cheng, Y.: Mean Shift, Mode Seeking, and Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 790–799 (1995)
Conn, A.R., Gould, N.I.M., Toint, P.L.: Trust-Region Methods. SIAM, Philadelphia (2000)
Isard, M., Blake, A.: Condensation – Conditional Density Propagation for Visual Tracking. International Journal of Computer Vision 29, 5–28 (1998)
Chen, H.T., Liu, T.L.: Trust-Region Methods for Real-Time Tracking. In: 8th IEEE International Conference on Computer Vision, vol. 2, pp. 717–722 (2001)
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© 2005 Springer-Verlag Berlin Heidelberg
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Deutsch, B., Gräßl, C., Bajramovic, F., Denzler, J. (2005). A Comparative Evaluation of Template and Histogram Based 2D Tracking Algorithms. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds) Pattern Recognition. DAGM 2005. Lecture Notes in Computer Science, vol 3663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550518_34
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DOI: https://doi.org/10.1007/11550518_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28703-2
Online ISBN: 978-3-540-31942-9
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