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.

Occlusionrobust Deformable Object Tracking without Physics Simulation Estimating the state of a deformable object is crucial for robotic manipulation, yet accurate tracking is challenging when the object is partiallyoccluded. To address this problem, we propose an occlusionrobust RGBD sequence tracking framework based on Coherent Point Drift (CPD). To mitigate the effects of occlusion, our method 1) Uses a combination of locally linear embedding and constrained optimization to regularize the output of CPD, thus enforcing topological consistency when occlusions create disconnected pieces of the object; 2) Reasons about the freespace visible by an RGBD sensor to better estimate the prior on point location and to detect tracking failures during occlusion; and 3) Uses shape descriptors to find the most relevant previous state of the object to use for tracking after a severe occlusion. Our method does not rely on physics simulation or a physical model of the object, which can be difficult to obtain in unstructured environments. Despite having no physical model, our experiments demonstrate that our method achieves improved accuracy in the presence of occlusion as compared to a physicsbased CPD method while maintaining adequate runtime.