A novel ensemble method to integrate with different technologies for moving foreground detection
Yi-Tung Chan, Shuenn-Jyi Wang, Chung-Hsien Tsai, Peiru Lin, Wen‐Pin Chen · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2015
Moving foreground detection can be used for the intelligent surveillance system and computer vision as an important step for many applications. Previous researchers have developed many different moving foreground detection technologies, such as background subtraction and optical flow. However, as far as we knew, there was few literature investigated ensemble method in integrate with various foreground detection technologies in real-time. In this paper, we present a new approach inspired from the ensemble system of machine learning to detect moving foreground by using weighted matrix with spatial characteristics. Furthermore, the weighted values can be automatically scaled over time for optimal flexibility and parameterization in our method. The experimental results demonstrate that the proposed method can not only provide compared performance with the state-of-the-art methods, but also satisfy real-time applications.