Visual Object Tracking With Partition Loss Schemes

Abstract

Object tracking is a fundamental task for building vision systems of automatic transportation. Despite demonstrated success in this active research field, it is still difficult to cope with complicated appearance changes caused by background clutters, illumination change, scale variation, deformation, rotation, and occlusion, etc. Due to these challenging factors, a target bounding box tends to easily contain a disturbing context of background, which may lead to an inaccurate localization if some key parts of the foreground object share an excess of information loss. In this paper, we propose an online algorithm using the local loss features to alleviate the drift problem during tracking. An adaptive block-division appearance model is constructed to exploit patch-based loss representations by decomposing sample region sequences into a set of subblocks. The basic purpose of partition coefficients is to indicate local relevance and effectively capture spatial correlation through measuring image similarity. Namely, they can strengthen positive effects of discriminative patches within locating bounding boxes and weaken negative impacts of disturbing context possibly included in the surrounding regions. The object state estimation of a moving target is then formulated as an integrated likelihood evaluation on the ensemble loss. Experimental results on a suite of representative video sequences of realistic scenarios demonstrate the superiority of the proposed method to several state-of-the-art tracking approaches in terms of both accuracy and robustness.