Probabilistic object tracking for global                                                            optimization          ...
Outline                         1. Introduction                         2. Bayesian filtering                         3. P...
1.- Introduction                       Object tracking in video-surveillance applications                             Es...
2.- Bayesian filtering                       Model the problem as a propagation of random variables                      ...
2.- Bayesian filtering                       Infererence: estimate the values of the state vector (reality)              ...
2.- Bayesian filtering                         K-1                     K                       K+1        TIME   p(zk|xk):...
2.- Bayesian filtering                       Bayes’ rule                             Combine observation and prediction ...
3.- Particle filters                       Particle filters                             Sequential Monte Carlo (SMC) met...
3.- Particle filters                       Understanding particles                             Each particle represents ...
3.- Particle filters                       Importance sampling (weighted particles)                             CONDENSA...
Marcos Nieto, Carlos Cuevas and Luis Salgado, “Measurement-based Reclustering for           Multiple Object Tracking with ...
Marcos Nieto, Carlos Cuevas and Luis Salgado, “Measurement-based Reclustering for           Multiple Object Tracking with ...
Marcos Nieto, Carlos Cuevas and Luis Salgado, “Measurement-based Reclustering for           Multiple Object Tracking with ...
Carlos R. del Blanco, Fernando Jaureguizar and Narciso García, “Visual Tracking of           Multiple Interacting Objects ...
R. Mohedano, N. García, “Robust Multi-Camera 3D Tracking from Mono-Camera 2D           Tracking using Bayesian Association...
4.- MCMC                       Approximation with unweighted particles                             Markov Chain Monte Ca...
4.- MCMC                       Approximation with unweighted particles                             MCMC methods improve ...
4.- MCMC / Vehicle tracking                       iToll project                                                          ...
4.- MCMC / Vehicle tracking                       iToll project                             Variable perspective        ...
4.- MCMC / Vehicle tracking                                                                               20Marcos Nieto (...
4.- MCMC / Vehicle tracking                       Inferring 3D volume from 2D observations                             T...
4.- MCMC / Vehicle tracking                                                                               22Marcos Nieto (...
4.- MCMC / Vehicle tracking                                                                               23Marcos Nieto (...
4.- MCMC / Vehicle tracking                                                                               24Marcos Nieto (...
5.- Conclusions                       Object tracking can be modelled as an probabilistic                        inferenc...
Dr.-Ing. Marcos Nieto Doncel                                      Investigador/Researcher                                 ...
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Hoip10 presentación seguimiento de objetos_vicomtech

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Presentación de Vicomtech sobre seguimiento de objetos realizada durante las jornadas HOIP 2010 organizadas por la Unidad de Sistemas de Información e Interacción TECNALIA.

Más información en http://www.tecnalia.com/es/ict-european-software-institute/index.htm

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Hoip10 presentación seguimiento de objetos_vicomtech

  1. 1. Probabilistic object tracking for global optimization Dr.-Ing. Marcos Nieto Doncel Investigador/Researcher mnieto@vicomtech.orgMarcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10)
  2. 2. Outline 1. Introduction 2. Bayesian filtering 3. Particle filters • Human tracking • Multiple object tracking 4. MCMC • iToll project 5. ConclusionsMarcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10) 2
  3. 3. 1.- Introduction  Object tracking in video-surveillance applications  Estimate properties of the imaged objects  Propagate knowledge through time  Multiple object tracking is a challenge W W H L H (x0,y0) (x0,y0) 3Marcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10)
  4. 4. 2.- Bayesian filtering  Model the problem as a propagation of random variables through time  Each property is represented by a random variable  The set of properties define the object at time t: state vector W W H H (x0,y0) x0 y0 4Marcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10)
  5. 5. 2.- Bayesian filtering  Infererence: estimate the values of the state vector (reality) based on a sequence of observations (images) applying uncertainty models  Observation model: how the object is expected to appear in images • Observation equation: p(zk|xk)  Dynamic model: what type of motion the object is expected to show? • Dynamic equation: p(xk|xk-1)  Find posterior distribution p(xk|z1:k) and obtain a point-estimate p(xk|z1:k) -> xk • Mean, robust-mean, mode, median, etc. 5Marcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10)
  6. 6. 2.- Bayesian filtering K-1 K K+1 TIME p(zk|xk): Observation model zk-1 zk zk+1 MEASUREMENTS (VISIBLE) STATES xk-1 xk xk+1 (HIDDEN) p(xk|xk-1): Dynamic model 6Marcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10)
  7. 7. 2.- Bayesian filtering  Bayes’ rule  Combine observation and prediction models Observation Prior  Under some assumptions, one can obtain the optimal solution to this problem • Kalman filter if distributions are Gaussians and models are linear  However, typical video-surveillance problems are highly non-linear and/or non-Gaussian • Sub-optimal solutions need to be computed 7Marcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10)
  8. 8. 3.- Particle filters  Particle filters  Sequential Monte Carlo (SMC) method, aka • Condensation algorithm, bootstrap filter, survival of the fittest…  It is a technique for implementing a recursive Bayesian filter by representing the posterior density as a set of samples or particles WEIGHTED UNWEIGHTED 8Marcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10)
  9. 9. 3.- Particle filters  Understanding particles  Each particle represents a hypothesis of the state-vector  The set of particles represents the “reliability” of each region of the space  The combination of particles lead to the best estimate W W H H (x0,y0) x0 y0 9Marcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10)
  10. 10. 3.- Particle filters  Importance sampling (weighted particles)  CONDENSATION, SIS, SIR 10Marcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10)
  11. 11. Marcos Nieto, Carlos Cuevas and Luis Salgado, “Measurement-based Reclustering for Multiple Object Tracking with Particle Filters,” in IEEE Proc. International Conference on Image Processing (ICIP2009). 3.- Particle filters / Human tracking ELLIPTICAL MODEL 11Marcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10)
  12. 12. Marcos Nieto, Carlos Cuevas and Luis Salgado, “Measurement-based Reclustering for Multiple Object Tracking with Particle Filters,” in IEEE Proc. International Conference on Image Processing (ICIP2009). 3.- Particle filters / Human tracking  Observation model Ratio between inside pixels and ellipse area Ratio between inside and total pixels Compactness around center of the ellipse 12Marcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10)
  13. 13. Marcos Nieto, Carlos Cuevas and Luis Salgado, “Measurement-based Reclustering for Multiple Object Tracking with Particle Filters,” in IEEE Proc. International Conference on Image Processing (ICIP2009). 3.- Particle filters / Human tracking  Multiple objects 13Marcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10)
  14. 14. Carlos R. del Blanco, Fernando Jaureguizar and Narciso García, “Visual Tracking of Multiple Interacting Objects Through Rao-Blackwellized Data Association Particle Filtering,” in IEEE Proc. International Conference on Image Processing (ICIP2010). 3.- Particle filters / Human tracking 14Marcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10)
  15. 15. R. Mohedano, N. García, “Robust Multi-Camera 3D Tracking from Mono-Camera 2D Tracking using Bayesian Association”, IEEE Trans. Consumer Electronics, vol. 56, no. 1, pp. 1-8, Feb. 2010. 3.- Particle filters / Human tracking 15Marcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10)
  16. 16. 4.- MCMC  Approximation with unweighted particles  Markov Chain Monte Carlo (MCMC)  Metropolis-Hastings algorithm to generate a Markov Chain that approximates to the posterior Metropolis-Hastings allows obtaining samples for an arbitrary distribution by making a chain which accepts or rejects samples 16Marcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10)
  17. 17. 4.- MCMC  Approximation with unweighted particles  MCMC methods improve the results obtained by importance sampling strategies for high dimensional spaces  MCMC is therefore recommended to be used when the dimensions of the problem increase  Metropolis-Hastings is the most typically used generic sampling algorithm for MCMC  Other alternatives • Slice sampler • Gibbs sampler • Levenberg-Marquardt 17Marcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10)
  18. 18. 4.- MCMC / Vehicle tracking  iToll project 18Marcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10)
  19. 19. 4.- MCMC / Vehicle tracking  iToll project  Variable perspective  Variable vehicle sizes, appearance and motion  Adverse illumination and weather conditions  Real-time operation 19Marcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10)
  20. 20. 4.- MCMC / Vehicle tracking 20Marcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10)
  21. 21. 4.- MCMC / Vehicle tracking  Inferring 3D volume from 2D observations  There is a projective ambiguity that can not be solved analytically There are 3D infinite volumes that project onto the same 2D shape But only one is the right one!  MCMC-based approach • Combination of projective geometry constraints • Temporal coherence • Prior knowledge about vehicle configurations 21Marcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10)
  22. 22. 4.- MCMC / Vehicle tracking 22Marcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10)
  23. 23. 4.- MCMC / Vehicle tracking 23Marcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10)
  24. 24. 4.- MCMC / Vehicle tracking 24Marcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10)
  25. 25. 5.- Conclusions  Object tracking can be modelled as an probabilistic inference problem  Uncertainty is mathematically handled  Potentially any problem can be tackled like this!  Particle filters are a popular tool to solve non-linear and analytically intractable inference problems  Importance sampling algorithms (CONDENSATION)  MCMC methods represents a step forward solving high- dimensional problems  Optimization technique could be applied to reduce the computational complexity and allow real-time performance 25Marcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10)
  26. 26. Dr.-Ing. Marcos Nieto Doncel Investigador/Researcher mnieto@vicomtech.org http://marcosnieto.zymichost.com/ 26Marcos Nieto (mnieto@vicomtech.org) Hands-on Image Processing 2010 (HOIP’10)

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