Probabilistic Tracking with Adaptive Feature Selection.
Journal
17th International Conference on Pattern Recognition, ICPR 2004, Cambridge, UK, August 23-26, 2004.
Pages
736-739
Date Issued
2004
Author(s)
Abstract
We propose a color-based tracking framework that infers alternately an object's configuration and good color features via particle filtering. The tracker adaptively selects discriminative color features that well distinguish foregrounds from backgrounds. The effectiveness of a feature is weighted by the Kullback-Leibler observation model, which measures dissimilarities between the color histograms of foregrounds and backgrounds. Experimental results show that the probabilistic tracker with adaptive feature selection is resilient to lighting changes and background distractions.
SDGs
Type
conference paper