A New Descriptor for Keypoint-Based Background Modeling

Abstract

Background modeling is a preliminary task for many computer vision applications, describing static elements of a scene and isolating foreground ones. Defining a robust background model of uncontrolled environments is a current challenge since the model must manage many issues, e.g., moving cameras, dynamic background, bootstrapping, shadows, and illumination changes. Recently, methods based on keypoint clustering have shown remarkable robustness especially in bootstrapping and camera movements, highlighting however limitations in the analysis of dynamic background (i.e., trees blowing in the wind or gushing fountains). In this paper, an innovative combination between the RootSIFT descriptor and an average pooling is proposed in a keypoint clustering method for real-time background modeling and foreground detection. Compared to renowned descriptors, such as A-KAZE, this combination is invariant to small local changes in the scene, thus resulting more robust in dynamic background cases. Results, obtained on experiments carried out on two benchmark datasets, demonstrate how the proposed solution improves the previous keypoint-based models and overcomes several works of the current state-of-the-art.