Foreground Object Detection in Changing Background Based on Color Co-Occurrence Statistics

This paper proposes a novel method for detecting foreground objects in nonstationary complex environments containing moving background objects. We derive a Bayes decision rule for classification of background and foreground changes based on inter-frame color co-occurrence statistics. An approach to store and fast retrieve color co-occurrence statistics is also established. In the proposed method, foreground objects are detected in two steps. First, both foreground and background changes are extracted using background subtraction and temporal differencing. The frequent background changes are then recognized using the Bayes decision rule based on the learned color co-occurrence statistics. Both short-term and longterm strategies to learn the frequent background changes are proposed. Experiments have shown promising results in detecting foreground objects from video containing wavering tree branches and flickering screens/water surface. The proposed method has shown better performance as compared with two existing methods.