TRACKING POORLY MODELLED MOTION USING PARTICLE FILTERS WITH ITERATED LIKELIHOOD WEIGHTING

Human motion in cluttered scenes is often tracked using particle filtering. However, poorly modelled inter-frame motion is not uncommon, resulting in poor priors for the filtering step. Alternatives to the Condensation algorithm in the form of an Auxiliary Particle Filter (APF) and Iterated Likelihood Weighting (ILW) are described. Experimental results comparing these filters’ accuracy and consistency are presented for a scenario in which a person is tracked in an overhead view using an ellipse model with a likelihood based on colour and gradient cues. ILW is not intended to give unbiased estimates of a posterior but rather to reduce approximation error. It is shown to outperform both Condensation and the APF on sequences from this scenario.

[1]  Michael Isard,et al.  ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework , 1998, ECCV.

[2]  Stephen J. McKenna,et al.  Head Tracking and Action Recognition in a Smart Meeting Room , 2003 .

[3]  Andrew Blake,et al.  Articulated body motion capture by annealed particle filtering , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[4]  David J. Fleet,et al.  People tracking using hybrid Monte Carlo filtering , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[5]  Yong Rui,et al.  Better proposal distributions: object tracking using unscented particle filter , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[6]  Michael Isard,et al.  Object localization by Bayesian correlation , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[7]  Michael Isard,et al.  Contour Tracking by Stochastic Propagation of Conditional Density , 1996, ECCV.

[8]  Tieniu Tan,et al.  Articulated model based people tracking using motion models , 2002, Proceedings. Fourth IEEE International Conference on Multimodal Interfaces.

[9]  Yoram Baram,et al.  The Bias-Variance Dilemma of the Monte Carlo Method , 2001, ICANN.

[10]  Vladimir Pavlovic,et al.  Learning Switching Linear Models of Human Motion , 2000, NIPS.

[11]  Stanley T. Birchfield,et al.  Elliptical head tracking using intensity gradients and color histograms , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[12]  M. Pitt,et al.  Filtering via Simulation: Auxiliary Particle Filters , 1999 .

[13]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[14]  Michael Isard,et al.  Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking , 2000, ECCV.

[15]  Michael J. Black,et al.  Learning and Tracking Cyclic Human Motion , 2000, NIPS.

[16]  Ian D. Reid,et al.  Automatic partitioning of high dimensional search spaces associated with articulated body motion capture , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[17]  P. Fearnhead,et al.  An improved particle filter for non-linear problems , 1999 .

[18]  Andrew Blake,et al.  A Probabilistic Exclusion Principle for Tracking Multiple Objects , 2004, International Journal of Computer Vision.

[19]  David A. Forsyth,et al.  How Does CONDENSATION Behave with a Finite Number of Samples? , 2000, ECCV.