Generation of Background Model Image Using Foreground Model

Proper consideration of the temporal domain and the spatial domain is essential to perform robust foreground object detection in visual surveillance. However, there are difficulties in considering long-term temporal information with CNN-based methods. To solve this limitation, classical algorithms and some deep learning-based algorithms have used a background model image. However, acquiring a sophisticated background model image is also one of the complex problems. Most of the algorithms take a lot of time to initialize the background model image and generate many errors in the presence of a static foreground. This paper proposes an algorithm for generating a background model image using a deep-learning-based segmenter to solve this problem. The proposed method shows a 66.25% lower mean square error (MSE) than the background subtraction (BGS) algorithm and 79.25% lower than the latest deep learning algorithm in the SBI dataset. In addition, in the deep learning-based segmenter that uses a background image as input, replacing the background image of BGS algorithm with the background image of the proposed method shows a 38.63% reduction in the false detection rate (PWC).