Learning Contextual Variations for Video Segmentation

摘要

This paper deals with video segmentation in vision systems. We focus on the maintenance of background models in long-term videos of changing environment which is still a real challenge in video surveillance. We propose an original weakly supervised method for learning contextual variations in videos. Our approach uses a clustering algorithm to automatically identify different contexts based on image content analysis. Then, state-of-the-art video segmentation algorithms (e.g. codebook, MoG) are trained on each cluster. The goal is to achieve a dynamic selection of background models. We have experimented our approach on a long video sequence (24 hours). The presented results show the segmentation improvement of our approach compared to codebook and MoG.