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

Abstract

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.

Author

Wenjun ZHOU†, Shun’ichi KANEKO† (Member), Dong LIANG††, Manabu HASHIMOTO†††, Yutaka SATOH††††
† Graduate School of Information Science and Technology, Hokkaido University , †† Nanjing University of Aeronautics and Astronautics, China , ††† Chukyo University , †††† National Institute of Advanced Industrial Science and Technology

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