Princeton Tracking Benchmark
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This website provides a benchmark comparison of tracking algorithms for generic objects, and our baseline algorithm for RGBD tracking. It contains: [UPDATE]: Now you can see the detailed result by clicking the algorithm title on the evaluation page or after your upload your result


If you report performance results, we request that you cite the following paper:

Shuran Song and Jianxiong Xiao.
Tracking Revisited using RGBD Camera: Unified Benchmark and Baselines
Proceedings of 14th IEEE International Conference on Computer Vision (ICCV2013)
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Despite significant progress, tracking is still considered to be a very challenging task. Recently, the increasing popularity of depth sensors has made it possible to obtain reliable depth easily. This may be a game changer for tracking, since depth can be used to prevent model drift and handle occlusion. We also observe that current tracking algorithms are mostly evaluated on a very small number of videos collected and annotated by different groups. The lack of a reasonable size and consistently constructed benchmark has prevented a persuasive comparison among different algorithms. In this paper, we construct a unified benchmark dataset of 100 RGBD videos with high diversity, propose different kinds of RGBD tracking algorithms using 2D or 3D model, and present a quantitative comparison of various algorithms with RGB or RGBD input. We aim to lay the foundation for further research in both RGB and RGBD tracking, and our benchmark is available at