Background Subtraction Based on Co-occurrence Pixel-Block Pairs for Robust Object Detection in Dynamic Scenes

This paper presents a novel background subtraction method called co-occurrence pixelblock pairs (CPB) for detecting objects in dynamic scenes. Based on a “pixel to block” structure, it uses the correlation of multiple co-occurrence pixel block pairs to detect objects in dynamic scenes. It offers robust background subtraction against a dynamically changing background. We firstly propose a correlation measure for co-occurrence pixel-block pairs to realize a robust background model. We then introduce a novel evaluation strategy named correlation depended decision function for accurate object detection with the correlation of co-occurrence pixel-block pairs. Finally, CPB can estimate the foreground from a dynamic background with a sensitive criterion. We describe our CPB in full detail and compare it to other background subtraction approaches. Experimental results with several challenging datasets demonstrate the effective performance of our CPB.