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Probabilistic Tracking and Recognition
of Non-rigid Hand Motion
Huang Fei, Ian ReidDepartment 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 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
• Tracking Global Region and Articulated Motion
Experiments
Frame 1Frame 1 Frame 2Frame 2 Frame 3Frame 3 Frame 4Frame 4 Frame 5Frame 5
Frame 6Frame 6 Frame 7Frame 7 Frame 8Frame 8 Frame 9Frame 9 Frame 10Frame 10
Frame 11Frame 11 Frame 12Frame 12 Frame 13Frame 13 Frame 14Frame 14 Frame 15Frame 15
• Coping with Occlusion Clutter
Frame 1Frame 1 Frame 2Frame 2 Frame 3Frame 3 Frame 4Frame 4 Frame 5Frame 5
Frame 6Frame 6 Frame 7Frame 7 Frame 8Frame 8 Frame 9Frame 9 Frame 10Frame 10
Frame 11Frame 11 Frame 12Frame 12 Frame 13Frame 13 Frame 14Frame 14 Frame 15Frame 15
Conclusion
• Two Independent Dynamic Processes • Two Bayesian Tracker =>Joint Bayes Filter• Robust Global Region Estimator• Robust State-Based Inference