Moving object detection using keypoints reference model

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

This article presents a new method for background subtraction (BGS) and object detection for a real-time video application using a combination of frame differencing and a scale-invariant feature detector. This method takes the benefits of background modelling and the invariant feature detector to improve the accuracy in various environments. The proposed method consists of three main modules, namely, modelling, matching and subtraction modules. The comparison study of the proposed method with a popular Gaussian mixture model proved that the improvement in correct classification can be increased up to 98% with a reduction of false negative and true positive rates. Beside that the proposed method has shown great potential to overcome the drawback of the traditional BGS in handling challenges like shadow effect and lighting fluctuation.

Original languageEnglish
Article numberA001
Pages (from-to)1-8
Number of pages8
JournalEurasip Journal on Image and Video Processing
Volume2011
Issue number1
DOIs
Publication statusPublished - 2011

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Detectors
Lighting
Object detection

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Information Systems

Cite this

Moving object detection using keypoints reference model. / Wan Zaki, Wan Mimi Diyana; Hussain, Aini; Hedayati, Mohamed.

In: Eurasip Journal on Image and Video Processing, Vol. 2011, No. 1, A001, 2011, p. 1-8.

Research output: Contribution to journalArticle

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