A Light Weight Approach for Real-time Background Subtraction in Camera Surveillance Systems

Real time processing in the context of image processing for topics like motion detection and suspicious object detection requires processing the background more times. In this field, background subtraction solutions can overcome the limitations caused by real time issues. Different methods of background subtraction have been investigated for this goal. Although more background subtraction methods provide the required efficiency, they do not make produce a real-time solution in a camera surveillance environment. In this paper, we propose a model for background subtraction using four different traditional algorithms; ViBe, Mixture of Gaussian V2 (MOG2), Two Points, and Pixel Based Adaptive Segmenter (PBAS). The presented model is a lightweight real time architecture for surveillance cameras. In this model, the dynamic programming logic is used during preprocessing of the frames. The CDnet 2014 data set is used to assess the model's accuracy, and the findings show that it is more accurate than the traditional methods whose combinations are suggested in the paper in terms of Frames per second (fps), F1 score, and Intersection over union (IoU) values by 61.31, 0.552, and 0.430 correspondingly.