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Collaborative Particle Filters for Group Tracking

Collaborative Particle Filters for Group Tracking. Loris Bazzani*, Marco Cristani*†, Vittorio Murino*† Speaker: Diego Tosato * *Computer Science Department, University of Verona, Italy †Istituto Italiano di Tecnologia (IIT), Genova, Italy.

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Collaborative Particle Filters for Group Tracking

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  1. Collaborative Particle Filters for Group Tracking Loris Bazzani*, Marco Cristani*†, Vittorio Murino*† Speaker: Diego Tosato* *Computer Science Department, University of Verona, Italy †Istituto Italiano di Tecnologia (IIT), Genova, Italy This research is founded by the EU-Project FP7 SAMURAI,grant FP7-SEC- 2007-01 No. 217899

  2. Analysis of the problem (1) • Multi-Target Tracking: • Estimate the trajectories of objects of interest, keeping their identification over the time • Well-investigated problem • State-of-the-art methods are very effective and efficient • Multi-Group Tracking: • Estimate the trajectories of the groups of objects, keeping their identification over the time • Not Well-investigated problem • Few methods in the State of the art

  3. Analysis of the problem (2) • Why it is a hard task • Methods for multi-target tracking fails • Groups are highly structured entity • Hard to model the complex dynamics • Strong appearance variations over the time • Intra- and inter-group occlusions phenomena • What is a group? • Motivation: • Highlighting social behaviors among individuals

  4. Outline • Overview of the proposed method • Particle Filtering • Multi-Object Tracking (MOT) • Multi-Group Tracking (MGT) • Collaborative Particle Filters (Co-PF) • Results • Conclusions

  5. Overview of the proposed method • Two separate particle filters • Multi-object tracker (MOT) models each individual separately • Multi-group tracker (MGT) focuses on groups as atomic entities • Coupling of the two processes in a formal probabilistic framework Co-PF Model

  6. Particle Filtering for Target Tracking • Recursively calculating the posterior distribution • is defined by • The dynamical model • The observation model • The first frame distribution • Monte Carlo approximation by a set of weighted particles

  7. Multi-Object Tracking • Extension to Multi-target • Hybrid Joint-Separable (HJS) Filter [Lanz 2006] • Approximation to decompose the joint state space in single state spaces • HJS is efficient and models the interactions among targets • We “just” need to define • Single-object dynamical and the single-object observation models [Lanz 2006] O. Lanz, “Approximate bayesian multibody tracking,” IEEETPAMI, 28(9):1436–1449, 2006.

  8. Multi-Group Tracking • Use HJS filter • State of the group: Gaussian model • Observation model • Projection of the cylinder into the image • Histogram-based feature as descriptor • Dynamical model • : linear motion, perturbed by Gaussian noise • : Gaussian perturbation of its principal axes, i.e., by varying its eigenvalues and eigenvectors

  9. Collaborative Particle Filters • Inject the information collected by the MOT into the MGT • Marginalization over the MOT state space • After some approximations, we end up with • It is a combination of MOT and MGT posteriors at time (t-1) MOT posterior at time (t-1) Linking probability MGT posterior at time (t-1)

  10. Collaborative Particle Filters • The linking probability connect the MGT state space to the MOT state space • Approximation through the Mixed-memory Markov Process • Linking likelihood is decomposed in three components • Appearance similarity: distance between color histograms • Dynamics consistency: same direction between group and person • Group membership: spatial proximity between person and group Linking likelihood

  11. Results • Compare Co-PF against MGT (without collaboration) • An annotated dataset for group tracking does not exist • Quantitative evaluation on a synthetic dataset emulating real scenarios [Kasturi et al 2009] ATA = Average Tracking Accuracy MOTA = Multiple Object Tracking Accuracy MOTP = Multiple Object Tracking Precision FP = False Positive MO = Multiple Objects FN = False Negative TSR = Tracking Success Rate [Kasturi et al 2009] R Kasturi, D Goldgof, P Soundararajan, V Manohar, J Garofolo,R Bowers, M Boonstra, V Korzhova, and J Zhang,“Framework for performance evaluation of face, text, and vehicledetection and tracking in video: Data, metrics, and protocol,”IEEE TPAMI, 31(2):319–336, 2009.

  12. Results • Qualitative evaluation on publicly available dataset MGT PETS 2009 dataset http://www.cvg.rdg.ac.uk/PETS2009/a.html

  13. Results • Qualitative evaluation on publicly available dataset Co-PF PETS 2009 dataset http://www.cvg.rdg.ac.uk/PETS2009/a.html

  14. Results • Qualitative evaluation on publicly available dataset MGT PETS 2009 dataset http://www.cvg.rdg.ac.uk/PETS2009/a.html

  15. Results • Qualitative evaluation on publicly available dataset Co-PF PETS 2009 dataset http://www.cvg.rdg.ac.uk/PETS2009/a.html

  16. Conclusions • A probabilistic, collaborative framework for multi-group tracking have been proposed • Additional evidence on the individuals helps the group tracking in an effective way • The results prove that the collaboration between trackers improve the performances Future directions: • Collaboration on the other direction (MGT MOT) • Detection, split, and merge of the groups

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