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Joint anisotropic mean shift and consensus point feature correspondences for object tracking in video.

, , , and . ICME, page 1270-1273. IEEE, (2009)

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Online subspace learning on Grassmann manifold for moving object tracking in video., , and . ICASSP, page 969-972. IEEE, (2008)Robust Visual Object Tracking Using Multi-Mode Anisotropic Mean Shift and Particle Filters., , and . IEEE Trans. Circuits Syst. Video Techn., 21 (1): 74-87 (2011)Global optimization of video quality by improved rate control on IP-networks., , , and . VCIP, volume 5150 of Proceedings of SPIE, page 552-562. SPIE, (2003)Robust Object Tracking Using Particle Filters and Multi-region Mean Shift., , and . PCM, volume 5879 of Lecture Notes in Computer Science, page 393-403. Springer, (2009)Joint particle filters and multi-mode anisotropic mean shift for robust tracking of video objects with partitioned areas., , and . ICIP, page 4077-4080. IEEE, (2009)Face tracking using Rao-Blackwellized particle filter and pose-dependent probabilistic PCA., , , and . ICIP, page 853-856. IEEE, (2008)Moving Object Tracking from Videos Based on Enhanced Space-Time-Range Mean Shift and Motion Consistency., , , and . ICME, page 2002-2005. IEEE Computer Society, (2007)Error-resilient packet video coding using harmonic frame-expansions and temporal prediction., and . ICIP (1), page 185-186. IEEE, (2005)Joint anisotropic mean shift and consensus point feature correspondences for object tracking in video., , , and . ICME, page 1270-1273. IEEE, (2009)Network-Adaptive Frame-Expansion-Based Packet Video Coding for Erasure Channels., and . ICME, page 1334-1337. IEEE Computer Society, (2005)