Texture-based self-adaptive moving object detection technique for complex scenes

Abstract

Moving object detection is a fundamental and critical task in video surveillance systems. It is very challenging for complex scenes having dynamic background and illumination variations. In this paper, Local Binary Pattern (LBP) texture-based moving object detection algorithm is proposed which uses adaptive learning rate to deal with different rates of change in background. This algorithm deals with the strong dynamic background and illumination variations effectively. The performance of the proposed algorithm is evaluated on the challenging videos containing strong dynamic background and illumination variations using standard performance metrics. Experimental results show that the proposed method achieves more than 10% average improvement in accuracy compared over existing algorithms.