Static region classification using hierarchical finite state machine

The ability of most existing approaches to classify static regions such as abandoned and removed objects in images is affected by illumination and traffic volume because of several predefined threshold values. To reduce these effects, we propose an accurate static region classification method using a hierarchical finite state machine that consists of three layers. Each FSM is defined by a Mealy state machine, where a support vector machine (SVM) determines the state transition based on the current state and input features. Because the proposed method uses optimally trained by SVM classifiers, it does not require threshold values and guarantees better classification accuracy under severe environmental changes. In experiments, the proposed method provided much higher classification accuracy and lower false alarm rate than the state-of-the-art methods.