Visual localization and segmentation based on foreground/background modeling

In this paper, we propose a novel method to localize (or track) a foreground object and segment the foreground object from the surrounding background with occlusions for a moving camera. We measure the likelihood of a target position by using a combination of a generative model and a discriminative model, considering not only the foreground similarity to the target model but also the dissimilarity between the foreground and the background appearances. Object segmentation is treated as a binary labeling problem. A Markov Random Field (MRF) is employed to add a spatial smooth prior on the foreground/background patterns. We demonstrate the advantages of the proposed method on several challenging videos and compare our results with the results of several other popular methods. The proposed method has achieved good results.