Moving object detection using Gaussian background model and Wronskian framework

In this article, we have proposed a stable background model construction from a given video sequence. The constructed background model is compared with different image frames of the same sequence to detect moving objects. Here the background model is constructed by analyzing a sequence of linearly dependent image frames in Wronskian framework. The Wronskian based change detection model is further used to detect the changes between the constructed background scene and the considered target frame. The proposed scheme is an integration of Gaussian averaging and Wronskian change detection model. Gaussian averaging uses different modes which arise over time to capture the underlying richness of background; and is an approach for background building by considering temporal modes. Similarly, Wronskian change detection model uses a spatial region of support in this regard. The proposed scheme relies on spatio-temporal modes arising over time to build the appropriate background model by considering both spatial and temporal modes. The effectiveness of the proposed scheme is verified by comparing the results with those of some of the existing state-of-the-art background subtraction techniques on public benchmark databases.