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Model-based hand tracking using a hierarchical Bayesian filter.

Björn Stenger·Arasanathan Thayananthan·Philip H S Torr·Roberto Cipolla
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Abstract

This paper sets out a tracking framework, which is applied to the recovery of three-dimensional hand motion from an image sequence. The method handles the issues of initialization, tracking, and recovery in a unified way. In a single input image with no prior information of the hand pose, the algorithm is equivalent to a hierarchical detection scheme, where unlikely pose candidates are rapidly discarded. In image sequences, a dynamic model is used to guide the search and approximate the optimal filtering equations. A dynamic model is given by transition probabilities between regions in parameter space and is learned from training data obtained by capturing articulated motion. The algorithm is evaluated on a number of image sequences, which include hand motion with self-occlusion in front of a cluttered background.

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AlgorithmsArtificial IntelligenceBayes TheoremComputer SimulationHandHumansImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalInformation Storage and RetrievalModels, BiologicalModels, StatisticalMovementPattern Recognition, AutomatedPhotographyReproducibility of ResultsSensitivity and SpecificityVideo RecordingJournal ArticleResearch Support, Non-U.S. Gov't

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Björn Stenger, Arasanathan Thayananthan, Philip H S Torr, Roberto Cipolla. 2006. Model-based hand tracking using a hierarchical Bayesian filter.. https://doi.org/10.1109/tpami.2006.189

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