Efficient Adaptive Combination of Histograms for Real-Time Tracking

Abstract

We quantitatively compare two template-based tracking algorithms, Hager's method and the hyperplane tracker, and three histogram-based methods, the mean-shift tracker, two trust-region trackers, and the CONDENSATION tracker. We perform systematic experiments on large test sequences available to the public. As a second contribution, we present an extension to the promising first two histogram-based trackers: a framework which uses a weighted combination of more than one feature histogram for tracking. We also suggest three weight adaptation mechanisms, which adjust the feature weights during tracking. The resulting new algorithms are included in the quantitative evaluation. All algorithms are able to track a moving object on moving background in real time on standard PC hardware.