Dynamic ARMA-Based Background Subtraction for Moving Objects Detection

Abstract

Background subtraction is a prevailing method for moving object detection in videos with stationary backgrounds. However, accurate and real-time moving object detection is challenging in the presence of complex dynamic scenes. This paper presents a novel technique for background subtraction based on the dynamic autoregressive moving average (ARMA) model. Specifically, we utilize the temporal and spatial correlation of images in a video sequence to model each pixel to accurately model the background image's dynamic characteristics. In addition, we apply an adaptive least mean square (LMS) scheme to update the parameters of the background model to offset the dramatically dynamic characteristic of the background. The proposed algorithm is evaluated on two publicly available benchmark datasets with complex dynamic backgrounds. The experimental results show that this technique is robust and effective for background subtraction in complex dynamic backgrounds and is a promising moving object detection scheme for real-time visual surveillance.

Keywords

• Background subtraction
• image segmentation
• ARMA model
• moving object detection
• adaptive LMS
• real-time visual surveillance