A foveated vision framework for visual change detection using motion and textural features

Change detection is an important process in many video-based applications such as anomaly event detection and video surveillance. This paper proposes a foveated vision framework that simulates the human visual system for change detection. It contains two phases—first identifying regions with visual changes due to significant motion, and then, the extraction of detailed information of the change. In phase I, change proposals (CPs) and background are segregated by analyzing the intensity and motion features. In phase II, visual changes are estimated from the CPs by analyzing the photometric and textural features. Each phase of analysis has a unique pre-generated archetype. A probabilistic refinement scheme is used to rectify the labeling of background and change. In each phase of analysis, the result is used to update the archetype immediately. Some well-known and recently proposed background modeling/subtraction algorithms are selected for our comparative study. Experimentations are performed on various video datasets. In some videos, our method can achieve higher accuracy than some recently proposed methods by 30%. In the large-scale experimentation using all the testing videos, our method can achieve higher average accuracy than the second best method by more than 3%.