Cauchy Mixture Model-based Foreground Object Detection with New Dynamic Learning Rate Using Spatial and Statistical information for Video Surveillance Applications

This paper presents a background modeling technique based on Cauchy mixture model (CMM) for moving object detection. The proposed approach detects the objects under different scenarios such as cluttered background, sudden and varied illumination, in the presence of shadow, slow- and fast-moving objects and under different weather conditions. The qualitative and quantitative performance evaluation of the proposed method on background model challenge (BMC) datasets, containing both real and synthetic videos, exhibits a superior performance over the state-of-the-art methods. The average accuracy for real and synthetic videos is 94.92% and 98.01%, respectively. The average F-score for real videos is 96.49%, and that for synthetic videos is 98.86%. The area under the curve (AUC) reveals an improved performance of 4.6% and 3.6% for real videos and synthetic videos of BMC dataset, respectively.