Dynamic background modeling based on radial basis function neural networks for moving object detection

Motion detection, the process which segments moving objects in video streams, is the first critical process of the automatic video surveillance system. However, the accuracy of this significant process is usually reduced by the dynamic scenes, which are commonly encountered in both indoor and outdoor situations. In this paper, the accurate motion detection is achieved by the proposed method based on a radial basis function neural network. Our method involves a multi background generation module and a moving object detection module. In the first module, the flexible multi-background model is generated by an unsupervised learning process to fulfil the property of either dynamic or static backgrounds. Next, the moving object detection module computes the binary object detection mask as the final result through the applied suitable threshold value. The detection results of our proposed method were compared with other state-of-the-art methods through qualitative visual inspection and quantitative estimation. The overall results show that the proposed method substantially outperforms existing methods by Similarity and F₁ accuracy rates of up to 82.08% and 86.75%, respectively.