Robust Object Segmentation using Adaptive Thresholding

This paper proposes a robust object segmentation algorithm that tackles problems that arise in environments in which the foreground and background colours are similar and there is light reflection in the shadow areas. The proposed algorithm bases its efficiency on a novel RGB colour detection process with adaptive threshold and edge detection, which are combined in order to obtain a foreground map containing objects and shadows. Subsequently, edge information is used to remove the shadow regions that are out of the edge bounding boxes. Finally, a post-processing procedure is applied and, for offline detection, a temporal filter is included in order to retrieve the misclassified pixels. The experimental results demonstrate the superior performance of our algorithm in comparison to other existing methods.

1. Introduction

The extraction of moving objects from video sequences is a key operation for content-based video coding, multimedia content description, and intelligent signal processing. Several algorithms have been proposed for object detection in video surveillance applications [1]. For colour images, a popular approach is based on the assumption that each pixel of the background is a realization of a random variable with Gaussian distribution (SGM-Single Gaussian Model) [2]. The mean and covariance of the Gaussian distribution are independently estimated for each pixel. In order to adapt the frequent colour change of the same background pixel, a Multiple Gaussian Model (MGM) is used to represent the distribution of the background pixels [3]. But there are still some serious drawbacks of these methods, e.g., mistakenly subtracting objects that have colours similar to the background colour and misclassifying shadows as foreground.