Object and shadow separation using fuzzy Markov Random Field and local gray level co-occurence matrix based textural features

In this article, we propose a novel object detection technique that can separate the moving object from its shadow. In this regard, we initially built a background model by taking median of pixel values in the temporal direction. To suppress the effects of quick change in illumination, and color frequency variation of the textured background, we have extracted the RGB color and ten local features at each pixel location in the target image and background model. For background separation, a difference image is generated by considering pixel by pixel absolute difference of the thirteen dimensional target image frame and the constructed background model. This is followed by a spatial Markov Random Field (MRF) constrained fuzzy clustering to find the moving regions in the target frame. The maximum a´posteriori probability (MAP) of the MRF constrained fuzzy clustering provides a binary image, where the moving objects with the moving cast shadow are identified as one group and the background is obtained as another group. To segment the moving objects from its shadow we explore a three stage shadow analysis technique. It uses analysis of rg color chrominance property of shadow, local gray level feature based shadow processing followed by boundary refinement to separate out the moving objects from its shadows. The performance of the proposed scheme is evaluated by comparing it with the state-of-the-art techniques.