Effective background modelling and subtraction approach for moving object detection

This study presents a hierarchical background modelling and subtraction approach for real-time detection of moving objects. At the first level, a novel pixel-wise background modelling method is proposed for coarse detection. The method can dynamically assign the optimal number of components for each pixel with the borrow-lend strategy. And a flexible learning rate which is variable and different for each component is presented to adapt to scene changes. Additionally, a new mechanism using a framework of finite state machine is introduced to maintain and update the background models. At the second level, in order to deal with sudden illumination changes, a block-wise foreground validation approach is adopted for refined detection. The authors compare the proposed approach with state-of-the-art methods and experimental results under various scenes demonstrate the robustness and effectiveness of the proposed approach.