Object Detection Using Background Subtraction and Foreground Motion Estimation

Abstract

A method for detecting moving objects using a Markov random field (MRF) model is proposed, based on background subtraction. We aim at overcoming two major drawbacks of existing methods: dynamic background changes such as swinging trees and camera shaking tend to yield false positives, and the existence of similar colors in objects and their backgrounds tends to yield false negatives. One characteristic of our method is the background subtraction using the nearest neighbor method with multiple background images to cope with dynamic backgrounds. Another characteristic is the estimation of object movement, which provides robustness for similar colors in objects and background regions. From the viewpoint of the MRF, we define the energy function by considering these characteristics and optimize the function by graph cut. In most cases of our experiments, the proposed method can be implemented in (nearly) real time, and experimental results show favorable detection performance even in difficult cases in which methods of previous studies have failed.