Adaptive Background Modeling for Effective Ghost Removal and Robust Left Object Detection

Abstract

A background model using image subtraction in an intelligent video surveillance system could make severe errors in detection and tracking of objects due to changes of natural phenomena such as shadows and wind. Adaptive background models have been proposed in order to solve these problems, but most previous methods can make a ghost and sometimes miss the left objects which have stopped moving for a while. In this paper, we propose an adaptive background method to robustly track left objects and to effectively remove ghosts. The proposed method is based on background subtraction using adaptive median filtering and background update using motion information. In this method, the background is firstly updated based on the motion information in a pixel unit and secondly updated again based on the contour of the objects in a non-motion region unit. The method prevents the left objects from absorbing into the background and removes the ghosts quickly. In the experiments, we prove an effectiveness of our method through the comparison with the previous adaptive median filtering background subtraction.