Object Recognition with Spatially Correlated Occlusion

Object recognition under partial occlusion is an important problem that arises in many remote sensing and surveillance applications. Occlusion is often caused by objects with significant spatial extent, thereby resulting in spatial correlation as to which parts of objects are visible. In this paper, we develop a unified formulation for occluded object recognition using spatially correlated occlusion model. It unifies the three different correspondence models into one formulation, and extends several statistical object recognition algorithms proposed in recent literatures. Our preliminary experimental results show that recognition performance under occlusion is improved with our spatially correlated occlusion models when compared with alternative recognition algorithms. 1