1. Kalman Filter Results
Fred Astaire and Ginger Rogers Shall We Dance, MGM 1941
J. M. Rehg © 2003

2. Classical MHT Approach Properties Problems for figure tracking
Tree of Kalman Filters (e.g. Reid 1979)
Enumerate all assignments
Properties
Requires discrete features
p(z|x) is multimodal
Problems for figure tracking
No discrete feature-detector for articulated figure
Blip 1
Blip 2
Blip 3 ?
J. M. Rehg © 2003

3. Monte-Carlo Methods Approach Properties Problems for figure tracking:
Nonparametric (sample-based) pdf updated via SIR.
Particle filter for time series data
e.g. CONDENSATION algorithm (Isard & Blake 1996)
Properties
Can approximate arbitrary density functions
Number of samples exponential in size of state space
Problems for figure tracking:
Prohibitive number of samples are required due to
High dimensions (38 states)
Weak dynamical model (constant velocity)
J. M. Rehg © 2003

4. Likelihood from Image Measurements
Likelihood measure from squared pixel residual error:
Template registration locally maximizes likelihood measure.
0.01
0.9
J. M. Rehg © 2003

5. Multiple Hypothesis Tracking
Cham & Rehg, CVPR 99
Maximum Likelihood Result
Observation
p(zt|xt)
Bayes Rule
p(xt|Zt)
Prediction
p(xt|Zt-1)
p(zt|xt) x
p(xt|Zt-1) x
p(xt-1|Zt-1)
p(xt|Zt-1)
p(xt| zt) x
J. M. Rehg © 2003

6. Further Tracking Results
J. M. Rehg © 2003