Choice of similarity measure, likelihood function and parameters for histogram based particle filter tracking in CCTV grey scale video

Authors:
Peter Dunne;Bogdan Matuszewski
Affiliations:
AD Group, Daresbury Park, Daresbury, Warrington, WA44HS, UK and Applied Digital Signal and Image Processing Research Centre (ADSIP), School of Computing, Engineering and Physical Sciences, Univers ...;Applied Digital Signal and Image Processing Research Centre (ADSIP), School of Computing, Engineering and Physical Sciences, University of Central Lancashire, PR1 2HE, UK
Venue:
Image and Vision Computing
Year:
2011

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Abstract

The choice of particle filter dissimilarity distance measures and likelihood functions is considered in the context of object tracking in grey scale CCTV video. The geometrical interpretation of the Bhattacharyya coefficient and distance is reviewed and the relationships between the Bhattacharyya, Matusita, histogram intersection and @g^2 distances are examined. It is argued that as long as the likelihood function satisfies certain criteria its analytical form is not critical in the stated tracking context. This is demonstrated through an experimental comparison between the use of the standard Bhattacharyya distance/Gaussian likelihood combination and the potentially computationally simpler histogram intersection distance/triangular likelihood combination in particle filter tracking sequences. It is shown that the differences between the approaches are marginal when the likelihood criteria are applied. Whilst the analysis was focused on a specific application and context, we suggest that the findings will be of value to particle filter tracking in general.