Saliency-based discriminant tracking

Abstract

We propose a biologically inspired framework for visual tracking based on discriminant center surround saliency. At each frame, discrimination of the target from the back- ground is posed as a binary classification problem. From a pool of feature descriptors for the target and background, a subset that is most informative for classification between the two is selected using the principle of maximum marginal di- versity. Using these features, the location of the target in the next frame is identified using top-down saliency, complet- ing one iteration of the tracking algorithm. We also show that a simple extension of the framework to include motion features in a bottom-up saliency mode can robustly iden- tify salient moving objects and automatically initialize the tracker. The connections of the proposed method to existing works on discriminant tracking are discussed. Experimen- tal results comparing the proposed method to the state of the art in tracking are presented, showing improved perfor- mance. ©2009 IEEE.