Adaptive Background Modeling for Dynamics Background

Abstract

An increasing number of CCTV have been deployed in public and crime-prone areas as demand for automatic monitoring system is increasing to counterbalance the limitation of human monitoring. To have a good monitoring system in such places, a good background model is needed in order to reduce amount of the video processing needed for tracking, classification, counting and etc. This paper proposes an adaptive background modeling that is able to model a scene under review at real-time. The proposed modeling system is also expected to be able to handle dynamic backgrounds and common problems in detection methods. A novel patch-based background reconstruction based on highest frequency of occurrences assumption and past pixel observation is proposed. Contrast adjusting method is used to reduce the problem of incorrectly classified foreground which is shadow problem. The proposed algorithm is focused to be tested and analytically compared with the dynamic background at the indoor and outdoor environment. The main challenges of background subtraction such as illumination changes, geometrical changes, stationary moving object problem and high speed object problem are taken care of and extensively discussed in this paper. The experimental results show that the algorithm is able to reconstruct a background model and produce accurate and precise foreground that can be used for other processing stages.