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Saliency Based Discriminant Tracking

We consider tracking in the context of center-surround saliency mechanisms that are prevalent in biological vision. Discriminant tracking can be posed as a particular instance of this discriminant saliency: it provides a unified and principled framework for classifier design, target detection, automatic tracker initialization, and scale adaptation. The video clips are in Quicktime format (H.264), and a most recent version of Quicktime is required. Quicktime is a free software and can be downloaded from here. Comparison When Initial Target Position is Known [ram] [roadcrossing] [athlete] [motinas_toni_ill] [dtneu_nebel] [sylvester] [coke11] [ballroom] [tiger2] [karlsruhe] [skater] [caviar] [seq10] Legend Error Rates (0 =perfect tracking; 1= total tracking failure) Scale Adaptive Tracking [dirtbike] [speedboat] [gravel] [baseball] Average Tracking Error for IVT and DST Automatic Initialization and Tracking [dirtbike] [surfer] [dog] [skiing] [pedestrians] Comparison of Tracking Error with Automatic and Manual Initializations References Incremental learning for robust visual tracking, D. Ross, J. Lim, R. Lin, and M. Yang, In IJCV, 77(1-3), pp 125-141, May 2008. On-line selection of discriminative tracking features, R. Collins, Y. Liu, and M. Leordeanu, In IEEE PAMI, 27(10), pp 1631-1643, Oct 2005. Ensemble tracking, S. Avidan,In IEEE PAMI, 29(2), pp 261-271, 2007. Visual tracking with online multiple instance learning, B. Babenko, M.H. Yang, S. Belongie, In IEEE CVPR, 2009.  © SVCL