A Robust Local Binary Similarity Pattern for Foreground Object Detection
This addresses foreground object detection for video surveillance, but it is incremental as it builds on existing methods with hybrid improvements.
The paper tackles foreground object detection in video surveillance by proposing a robust texture operator (RLBSP) and combining color and texture features, achieving favorable performance against state-of-the-art methods on the CDnet 2012 dataset.
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.