A Robust Local Binary Similarity Pattern for Foreground Object Detection

TL;DR

This paper introduces a robust foreground object detection method that combines a new texture operator, RLBSP, with color features, effectively handling illumination changes and dynamic backgrounds in video surveillance.

Contribution

The paper presents RLBSP, a novel texture operator, and a combined color-texture approach, improving robustness and accuracy in foreground detection under challenging conditions.

Findings

Outperforms state-of-the-art methods on CDnet 2012 dataset

Demonstrates strong robustness to illumination variations

Effective handling of dynamic backgrounds

Abstract

Accurate and fast extraction of the foreground object is one of the most significant issues to be solved due to its important meaning for object tracking and recognition in video surveillance. Although many foreground object detection methods have been proposed in the recent past, it is still regarded as a tough problem due to illumination variations and dynamic backgrounds challenges. In this paper, we propose a robust foreground object detection method with two aspects of contributions. First, we propose a robust texture operator named Robust Local Binary Similarity Pattern (RLBSP), which shows strong robustness to illumination variations and dynamic backgrounds. Second, a combination of color and texture features are used to characterize pixel representations, which compensate each other to make full use of their own advantages. Comprehensive experiments evaluated on the CDnet 2012 dataset demonstrate that the proposed method performs favorably against state-of-the-art methods.