MAP Spatial Pyramid Mean Shift for Object Tracking Mean Shift is popular in object tracking due to its simplicity and efficiency. It finds local maximum of the similarity measure between the target model and target candidate, and works well in many situations. However, it suffers from two aspects. First, Mean Shift tracker ignores background knowledge. As a result, it may fail when the background color is similar to that of the target or the initial target region contains too much background. Second, Mean Shift tracker omits the geometric structure with a global color histogram as the target model. Therefore, it may not work in the case of partial occlusion. To solve the first problem, we introduce background color histogram into a MAP formulation. To address the second problem, we divide the target into hierarchical blocks. These blocks are described with a histogram each but tracked as a whole. The two threads lead to a new algorithm, named MAP spatial pyramid (MAPSP) Mean Shift. The efficiency of MAPSP Mean Shift is demonstrated via comparative experiments on both standard and our own video sequences.

Partial occlusion robust object tracking using an effective appearance model 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 blockdividing 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 stateoftheart algorithms.