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Ghost Detection and Removal Based on Two-Layer Background Model and Histogram Similarity.

Yiping Xu¹, Hongbing Ji¹, Wenbo Zhang¹

¹School of Electronic Engineering, Xidian University, Xi'an 710071, China.

Sensors (Basel, Switzerland)

|August 23, 2020

Summary

This summary is machine-generated.

This study introduces a novel method for detecting and removing ghosting artifacts in moving object detection. The proposed algorithm effectively eliminates ghosting, significantly improving overall detection performance.

Keywords:

background subtraction ghosts histogram similarity local binary similarity pattern (LBSP)motion detection sample-based background model

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Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Ghosting artifacts are a persistent challenge in moving object detection, degrading performance.
Existing methods struggle with the permanent nature of ghosting once formed.

Purpose of the Study:

To develop an effective algorithm for detecting and removing ghosting artifacts.
To improve the overall performance of moving object detection systems.

Main Methods:

Classified ghosting artifacts into two categories based on formation.
Proposed a sample-based two-layer background model for ghost detection.
Utilized histogram similarity of ghost areas for ghost removal.
Introduced automatic parameter determination using spatial-temporal pixel information.

Main Results:

Successfully detected and removed two types of ghosting artifacts.
Demonstrated superior performance compared to state-of-the-art approaches on the CDnet 2014 dataset.
Algorithm adapts rapidly to scene changes.

Conclusions:

The proposed ghost detection and removal algorithm is effective.
The method significantly enhances moving object detection performance.
The approach offers a robust solution for ghosting issues in video analysis.