Real-time 3-D human body tracking using variable length Markov models

Fabrice Caillette, Aphrodite Galata, Toby Howard

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


    In this paper, we introduce a 3-D human-body tracker capable of handling fast and complex motions in real-time. The parameter space, augmented with first order derivatives, is automatically partitioned into Gaussian clusters each representing an elementary motion: hypothesis propagation inside each cluster is therefore accurate and efficient. The transitions between clusters use the predictions of a Variable Length Markov Model which can explain highlevel behaviours over a long history. Using Monte-Carlo methods, evaluation of model candidates is critical for both speed and robustness. We present a new evaluation scheme based on volumetric reconstruction and blobs-fitting, where appearance models and image evidences are represented by Gaussian mixtures. We demonstrate the application of our tracker to long video sequences exhibiting rapid and diverse movements.
    Original languageEnglish
    Title of host publicationBMVC 2005 - Proceedings of the British Machine Vision Conference 2005|BMVC - Proc. Br. Mach. Vis. Conf.
    PublisherBMVA Press
    Publication statusPublished - 2005
    Event2005 16th British Machine Vision Conference, BMVC 2005 - Oxford
    Duration: 1 Jul 2005 → …


    Conference2005 16th British Machine Vision Conference, BMVC 2005
    Period1/07/05 → …


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