Partial occlusion robust object tracking using an effective appearance model

Abstract

Partial occlusion is one of the most challenging difficulties for object tracking. In this paper, we present an approach to address this problem by using an effective appearance model which has two innovations. First, in contrast to widely used color histogram that models the appearance of an object using only color information, we assert that both color and texture are important cues for tracking, especially in the presence of complex background. We thus propose a novel local descriptor, named local color texture pattern (LCTP), to model the appearance of the object with color and texture information simultaneously. Second, global color histogram completely ignores the spatial layout information of an object and are sensitive to partial occlusion. In this work, we overcome this limitation based on a block-dividing way: 1) divide target into multiple blocks and then represent each block with LCTP histogram, 2) with a selectivity strategy, we select blocks that are not occluded and then combine similarities of those selected blocks to obtain final similarity measure. Experimental results demonstrate that the proposed method is more robust to partial occlusion than two state-of-the-art algorithms.