Probabilistic Tracking and Recognition of Non-rigid Hand Motion

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Presentation transcript:

Probabilistic Tracking and Recognition of Non-rigid Hand Motion Huang Fei, Ian Reid Department of Engineering Science Oxford University

The Problem Simultaneous Tracking and Recognition Articulation and Self-Occlusion Cluttered Background Scene and Occlusion Two Successive Frames From A Video Sequence

Previous Research Kinematic Model v.s. Appearance Model Toyama & Blake “Metric Mixture Tracker” Merits: -Exemplar v.s. Model -Spatial-Temporal Filtering Disadvantages: -Contour (Edges) v.s. Region (Silhouettes) -Joint Observation Density of Two Independent Processes

Method System Diagram of Joint Bayes Filter

The Interaction Between Two Components in Joint Bayes Filter

Discrete Appearance Tracker Non-Rigid Appearances v.s. Speech Signal Assumption: -Representative Hand Appearances -Non-Rigid Motion Observe Markov Dependence The Aim of Learning: -Exemplar as Shape Tracker Representation -Articulated Human Motion Dynamics

Visualizing Non-Rigid Hand Motion Local Linear Embedding Algorithm (S.Roweis & L.Saul 2000)

Robust Region Tracker Use Probabilistic Colour Histogram Tracker (P.Prez et.al. ECCV 2002) As Global Region Estimator

Experiments Tracking Global Region and Articulated Motion Frame 1

Coping with Occlusion Clutter Frame 1 Frame 2 Frame 3 Frame 4 Frame 5 Frame 6 Frame 7 Frame 8 Frame 9 Frame 10 Frame 11 Frame 12 Frame 13 Frame 14 Frame 15

Conclusion Two Independent Dynamic Processes Two Bayesian Tracker =>Joint Bayes Filter Robust Global Region Estimator Robust State-Based Inference