A Modular Approach to the Analysis and Evaluation of Particle Filters for Figure Tracking

Ping Wang and James M. Rehg

Project description           Modular analysis of PFs           Testing Videos           Ground truth           Code download            Publications            Space-Time acquisition            Acknowledgements            Contact            Copyright

Project description 

This work presents the first systematic empirical study of the particle filter (PF) algorithms for human figure tracking in video. Our analysis and evaluation follows a modular approach which is based upon the underlying statistical principles and computational concerns that govern the performance of PF algorithms. Based on our analysis, we propose a novel PF algorithm for figure tracking with superior performance called the Optimized Unscented PF. We examine the role of edge and template features, introduce computationally-equivalent sample sets, and describe a method for the automatic acquisition of reference data using standard motion capture hardware. The software and test data are made publicly-available on our project website.

Modular analysis of selected Particle Filters

Video data and associated properties


Dancing clip 0,1,2 -- Foreshortening, self-occlusion, rotation, fast movement. clustered/moving background, lighting change

Walking (viewing angle=45) -- Foreshortening, self-occlusion

Jumping jacks -- Small foreshortening

Ground truth

Joint centers -- Joint center positions of the 2D figure in every frame. dancing clip 1,2, walking, jumping jacks

States -- State inferred from joint center ground truth. dancing clip 1,2, walking, jumping jacks

Software architecture and Code

Figure tracking code: patch-based, edge-based

Document of single hypothesis figure tracking, by Alton Patrick

Space-Time acquisition system

Link to Matt Flagg's work!


A Modular Approach to the Analysis and Evaluation of Particle Filters for Figure Tracking, Ping Wang and James M. Rehg, IEEE Computer Society Conference on Computer Vision and Pattern Recognition - CVPR'06, Volume 1, pp. 790-797, New York, June, 2006.


The authors thank Matthew Flagg and Young Ki Ryu for building the mocap system, and Alton Patrick for the figure tracking code. This work was supported by NSF Award IIS-0133779.


pingwang at gatech.edu


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Last updated on 10/05/06