Foreground-Background Segmentation Based on Codebook and Edge Detector

TL;DR

This paper enhances background modeling for moving object detection by integrating codebook segmentation with edge detection algorithms, demonstrating improved detection quality through comparative analysis.

Contribution

It introduces a novel combination of codebook segmentation with edge detection to improve moving object detection in video sequences.

Findings

Edge detection improves codebook segmentation accuracy

Combined method outperforms standalone algorithms in detection quality

Evaluation using frame-based metrics confirms effectiveness

Abstract

Background modeling techniques are used for moving object detection in video. Many algorithms exist in the field of object detection with different purposes. In this paper, we propose an improvement of moving object detection based on codebook segmentation. We associate the original codebook algorithm with an edge detection algorithm. Our goal is to prove the efficiency of using an edge detection algorithm with a background modeling algorithm. Throughout our study, we compared the quality of the moving object detection when codebook segmentation algorithm is associated with some standard edge detectors. In each case, we use frame-based metrics for the evaluation of the detection. The different results are presented and analyzed.