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

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

3. 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

4. Method
System Diagram of Joint Bayes Filter

5. The Interaction Between Two Components in
Joint Bayes Filter

6. 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

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

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

9. Experiments Tracking Global Region and Articulated Motion Frame 1

10. 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

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