1. Multi-scale Visual Tracking by Sequential Belief Propagation
Gang Hua, Ying Wu
Dept. Electrical & Computer Engr.
Northwestern University
Evanston, IL 60208
11/18/2018
CVPR'2004

2. Abrupt Motion sudden changes of target dynamics frame dropping
large camera motion
etc.
11/18/2018
CVPR'2004

3. Challenges
Most existing visual tracking methods assume either small motion or accurate motion models
Abrupt motion violates them
Hierarchical search is not enough
Unidirectional information flow
Error accumulation from coarse to fine
No mechanism to recover failure in coarse scales
11/18/2018
CVPR'2004

4. Our Idea Different scales provide different salient visual features
Bi-directional information flow among different scales should help
Different scales “collaborate”
11/18/2018
CVPR'2004

5. Our Formulation A Markov network
X={Xi ,i=1..L}—target state in different scales
Z={Zi ,i=1..L}—Image observation of the target in different scales
Undirected link— Potential function Ψij(fi(Xi),fj(Xj)),
Directed link—Observation function Pi(Zi|Xi)
The task is to infer Pi (Xi|Z), i=1..L
Fig.1. Markov Network (MN)
11/18/2018
CVPR'2004

6. Belief propagation (BP)
The joint posterior
Belief propagation [Pearl’88, Freeman’99]
11/18/2018
CVPR'2004

7. Dynamic Markov Network
Xt={Xt,i ,i=1..L}—Target states at time t
Zt={Zt,i ,i=1..L}—Image observations at time t
P(Xt,i|Xt-1,i)—Dynamic model in the ith scale
Zt={Zk, k=1..t}—Image observation up to time t
Fig.2. Dynamic Markov Network (DMN) modeling target dynamics
11/18/2018
CVPR'2004

8. Bayesian inference in DMN
Markovian assumption
The Bayesian inference is
Independent dynamics model
11/18/2018
CVPR'2004

9. Sequential BP Message Passing in DMN Belief update in DMN 11/18/2018
CVPR'2004

10. Sequential BP Monte Carlo
To handle non-Gaussian densities
Monte Carlo implementation
A set of collaborative particle filters
11/18/2018
CVPR'2004

11. Algorithm
11/18/2018
CVPR'2004

12. Experiments: bouncing ball
Sudden dynamics changes fail the single particle filters
The tracking result of the Sequential BP
11/18/2018
CVPR'2004

13. Experiments: dropping frames
Dropping 9/10 of the video frames
BP iteration at a specific time instant
11/18/2018
CVPR'2004

14. Experiments: shaking camera
11/18/2018
CVPR'2004

15. Experiments: scale changes
11/18/2018
CVPR'2004

16. Conclusion& future work
Contributions
A new multi-scale tracking approach
A rigorous statistical formulation
A sequential BP algorithm with Monte Carlo
Future work
Theoretic study& comparison of the BP with the mean field variational approach
Learning model parameters
11/18/2018
CVPR'2004