Effective Weighted Compressive Tracking

Abstract

Compressive Tracking (CT) model is a recently proposed method for visual tracking, in which the appearance model is constructed from the features selected from the multiscale image feature space based on compressive sensing. The CT tracker has been proven to be effective. However, since it does not discriminatively consider the sample importance in its learning procedure, the CT tracker may detect the less important positive samples and, therefore, suffer from drift. In this paper, we present a novel Weighted Compressive Tracking (WCT) model based on the CT tracker. The proposed WCT tracker integrates the sample importance into an efficient online learning procedure so that the features are much more discriminative. Experimental results on challenging benchmark image sequences demonstrate that the proposed WCT tracker performs more favorably than the CT tracker. In addition, the WCT and CT trackers are also applied to the video acquired by the fisheye lens, the result of WCT tracker is very promising, whereas the CT tracker fails.