Multi scale visual tracking by sequential belief propagation
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Multi-scale Visual Tracking by Sequential Belief Propagation. Gang Hua, Ying Wu Dept. Electrical & Computer Engr. Northwestern University Evanston, IL 60208 {yingwu,ganghua}@ece.northwestern.edu. Abrupt Motion. sudden changes of target dynamics frame dropping large camera motion

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Multi scale visual tracking by sequential belief propagation

Multi-scale Visual Tracking by Sequential Belief Propagation

Gang Hua, Ying Wu

Dept. Electrical & Computer Engr.

Northwestern University

Evanston, IL 60208

{yingwu,ganghua}@ece.northwestern.edu

CVPR'2004


Abrupt motion
Abrupt Motion

  • sudden changes of target dynamics

  • frame dropping

  • large camera motion

  • etc.

CVPR'2004


Challenges
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

CVPR'2004


Our idea
Our Idea

  • Different scales provide different salient visual features

  • Bi-directional information flow among different scales should help

  • Different scales “collaborate”

CVPR'2004


Our formulation
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)

CVPR'2004


Belief propagation bp
Belief propagation (BP)

  • The joint posterior

  • Belief propagation [Pearl’88, Freeman’99]

CVPR'2004


Dynamic markov network
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

CVPR'2004


Bayesian inference in dmn
Bayesian inference in DMN

  • Markovian assumption

  • The Bayesian inference is

  • Independent dynamics model

CVPR'2004


Sequential bp
Sequential BP

  • Message Passing in DMN

  • Belief update in DMN

CVPR'2004


Sequential bp monte carlo
Sequential BP Monte Carlo

  • To handle non-Gaussian densities

  • Monte Carlo implementation

  • A set of collaborative particle filters

CVPR'2004


Algorithm
Algorithm

CVPR'2004


Experiments bouncing ball
Experiments: bouncing ball

  • Sudden dynamics changes fail the single particle filters

The tracking result of the Sequential BP

CVPR'2004


Experiments dropping frames
Experiments: dropping frames

  • Dropping 9/10 of the video frames

BP iteration at a specific time instant

CVPR'2004




Conclusion future work
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

CVPR'2004