REAL-TIME ILLUMINATION COMPENSATION ON DYNAMIC BACKGROUND FOR CROWD ANALYTIC SURVEILLANCE - MOHAMED AMEEN MASHOOD, MOHAMED ABUL HASSAN (2014)
Background modeling is one of the key steps in any visual surveillance system. A good background modeling algorithm should be able to detect objects/targets under any environmental condition. The influence of illumination variance has been a major challenge in many background modeling algorithms. Previously developed algorithms which intend to solve this issue produced poor object segmentation or consumed substantial amount of computational time, which made them not implementable at real time. In this thesis we propose a novel background mod:elin0 ITtdhod b<1:>ed on Phase texture features. The proposed method uses Phase texture features to overcome the effect of illumination variance, while preserving efficient background/foreground segmentation and computational efficiency. The proposed method is been validated qualitatively and quantitatively using various crowd analytic databases. Moreover, the results of our method is been used with other behavior learning algorithms to improve their efficiency.