A Comparative Evaluation of Template and Histogram Based 2D Tracking Algorithms

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.

[1]  D HagerGregory,et al.  Efficient Region Tracking With Parametric Models of Geometry and Illumination , 1998 .

[2]  Nicholas I. M. Gould,et al.  Trust Region Methods , 2000, MOS-SIAM Series on Optimization.

[3]  Patrick Pérez,et al.  Color-Based Probabilistic Tracking , 2002, ECCV.

[4]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Gregory D. Hager,et al.  Efficient Region Tracking With Parametric Models of Geometry and Illumination , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Dorin Comaniciu,et al.  Bayesian Kernel Tracking , 2002, DAGM-Symposium.

[7]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[9]  Michel Dhome,et al.  Hyperplane Approximation for Template Matching , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Hwann-Tzong Chen,et al.  Trust-region methods for real-time tracking , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[11]  Hwann-Tzong Chen,et al.  Real-time tracking using trust-region methods , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.