An Effective Background Subtraction Method Based on Pixel Change Classification
Songyin Fu, Gangyi Jiang, Mei Yu · 2010
Background subtraction is an effective way which is commonly used in intelligent monitoring system for extraction of moving objects. However, the key step of background subtraction need for a precise and time-varying background model. In this paper, we describe an improved background model with its updating method, and apply to a computer vision-based motion detection system able to detect moving objects in real-time. Our system first establishes an background based on Gaussian model; second computes the set of statistical parameters of background model according to the way pixel changes; and then extracts moving objects using the updated background model. Finally experimental results and a performance measure establishing the confidence of the method are presented.