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Moving Object Detection for Visual Surveillance Using Quasi-euclidian Distance

  • Dileep Kumar Yadav
  • Karan Singh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 381)

Abstract

Moving object detection is a fundamental step for visual surveillance system, other image processing, and computer vision applications. The most popular and common technique for moving foreground detection is background subtraction. In dynamic background, Gaussian Mixture Model performs better for object detection. In this work, a GMM-based background model is developed. This work proposes a quasi-euclidian distance measure in order to measure the variation in terms of distance, between modeled frame and test frame. To classify the pixel, this distance is compared with a suitable threshold. The connected component and blob labeling has been used to improve the model with a threshold. Morphological filter is used to improve the foreground information. The experimental study shows that the proposed work performs better in comparison to considered state-of-the-art methods in term precision, recall, and f-measure.

Keywords

Gaussian mixture model Quasi-euclidian distance Object detection Visual surveillance Morphological filter 

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Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.School of Computer and Systems Sciences, JNUNew DelhiIndia

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