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Outline of the Talk

Integration of shape constraints in data association filters Giambattista Gennari, Alessandro Chiuso, Fabio Cuzzolin, Ruggero Frezza University of Padova chiuso@dei.unipd.it www.dei.unipd.it/~chiuso. Outline of the Talk. Tracking and Data Association Classical solution: independent dynamics

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Outline of the Talk

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  1. Integration of shape constraints in data association filtersGiambattista Gennari, Alessandro Chiuso, Fabio Cuzzolin, Ruggero FrezzaUniversity of Padovachiuso@dei.unipd.itwww.dei.unipd.it/~chiuso IEEE CDC 2004 - Nassau, Bahamas, December 14-17

  2. Outline of the Talk • Tracking and Data Association • Classical solution: independent dynamics • Our approach : integration of shape • Occlusions • Experiments IEEE CDC 2004 - Nassau, Bahamas, December 14-17

  3. Tracking and Data Association • PROBLEM:Set of targets generating UNLABELLED measurements Associate and Track • Occlusions • Clutter IEEE CDC 2004 - Nassau, Bahamas, December 14-17

  4. SHAPE AND COORDINATION Motion invariant properties of targets: • Rigid or Articulated bodies • Formations of vehicles • (Flock of birds) • Deformable objects Distances and/or angles Connectivity – distances Relative velocity Group of admissible deformations (probabilistic or deterministic) IEEE CDC 2004 - Nassau, Bahamas, December 14-17

  5. Probabilistic Tracking and Data Association CLASSICALLY: JPDAF – MHT + Dynamical Models OUR APPROACH: JPDAF- (MHT) + Independent Dynamical Models + Shape Information Full (joint) model -not flexible -computationally expensive Model targets Independently -flexible and easy -not robust occlusions exchange tracks + Flexible + Robust to occlusions and track proximity - Computation (Monte Carlo) IEEE CDC 2004 - Nassau, Bahamas, December 14-17

  6. Independent Motion • Targets are described by independent dynamics Index of Target • Flexible and easy • Lack of robustness in presence of occlusions, false detections and closely spaced targets IEEE CDC 2004 - Nassau, Bahamas, December 14-17

  7. Associations • An association is a map matching unlabelled measurements to targets • Employ the overall model to compute the probability of each association Measurements Measurements matched to clutter Association Measurements matched to targets IEEE CDC 2004 - Nassau, Bahamas, December 14-17

  8. Description of “Shape” Probabilistic Model Motion Invariant Targets positions • Prior Knowledge • Learn from Data • Example: pairwise distances of non perfectly rigid bodies IEEE CDC 2004 - Nassau, Bahamas, December 14-17

  9. Shape Integration • We assume the overall model can be factored into two terms describing the mutual configuration and single target dynamics Shape constraints Kalman filters and independent dynamical models IEEE CDC 2004 - Nassau, Bahamas, December 14-17

  10. Occlusions • To compute marginalize over the occluded : Detected points Missing points (occlusions) • Compute the integral through Monte Carlo techniques IEEE CDC 2004 - Nassau, Bahamas, December 14-17

  11. Monte Carlo Integration • Sample: • Weight: • Integrate: • Fair sample from the posterior IEEE CDC 2004 - Nassau, Bahamas, December 14-17

  12. Summary Conditional state estimates INDEPENDENT KALMAN FILTERS SHAPE …. T1 T2 TN Association probabilities OVERALL MODEL Monte Carlo fair samples for occluded points state estimation Measurements IEEE CDC 2004 - Nassau, Bahamas, December 14-17

  13. State Estimation • An overall state estimate can be obtained summing the conditional state estimates weighted by the corresponding association probabilities • Alternatively, several state estimates can be propagated over time (multi hypothesis tracker ) Necessary in the learning phase ! IEEE CDC 2004 - Nassau, Bahamas, December 14-17

  14. Results • Real data from a motion capture system • Rapid motion • High numbers of false detections • Occlusions lasting several frames IEEE CDC 2004 - Nassau, Bahamas, December 14-17

  15. Results Commercial system: looses and confuses tracks With shape knowledge learned from data IEEE CDC 2004 - Nassau, Bahamas, December 14-17

  16. Conclusions • Algorithm for integrating shape knowledge into data association filter • Robust in presence of occlusions and clutter • Provide a framework for learning shape models (this requires use of multiple hypothesis kind of algorithms) • (In the example shape was learned from data) IEEE CDC 2004 - Nassau, Bahamas, December 14-17

  17. IEEE CDC 2004 - Nassau, Bahamas, December 14-17

  18. Shape Constraints • In many cases, coordinated points exhibit properties which are invariant with respect to their motion, they satisfy some sort of shape constraints: • pairwise distances of rigidly linked points are constant • the position and velocity of a point moving in group are similar to those of its neighbors IEEE CDC 2004 - Nassau, Bahamas, December 14-17

  19. Coordinated Motion • Rigid motion • Articulated bodies, • Groups of people moving together, • Formations • Taking into account coordination improves tracking robustness • We describe shape and motion separately and combine them together ( more flexible than joint models ) IEEE CDC 2004 - Nassau, Bahamas, December 14-17

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