Occlusion as a common phenomenon in object surface can seriously affect information collection of light field. To visualize light field data-set, occlusions are usually idealized and neglected for most prior light field rendering (LFR) algorithms. However, the 3D spatial structure of some features may be missing to capture some incorrect samples caused by occlusion discontinuities. To solve this problem, we propose an occlusion probability (OCP) model to improve the capturing information and the rendering quality of views with occlusion for the LFR. In this OCP model, a probability density model is applied to obtain the scores of visibility are modeled as hidden variables. The occlusion probability is calculated by the visibility, position and orientation of camera. We compare different capturing/reconstruction techniques to visualize/manipulate our OCP model.