Publication:
Background Subtraction In Urban Traffic Video Using Recursive Sigma-delta Mixture Model

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dc.contributor.authorMa`moun Al- Smadien_US
dc.contributor.authorKhairi Abdulrahimen_US
dc.contributor.authorRosalina Abdul Salamen_US
dc.contributor.authorAhmad Alajarmehen_US
dc.date.accessioned2024-05-28T05:51:23Z
dc.date.available2024-05-28T05:51:23Z
dc.date.issued2016
dc.descriptionVolume:11 Issue:3en_US
dc.description.abstractMotion segmentation is a fundamental step in urban traffic surveillance systems, since it provides necessary information for further processing. Background subtraction techniques are widely used to identify foreground moving vehicles from static background scene. Conventional techniques utilize single background model or Gaussian mixture model, which involves either poor adaptation or high computation.The complexity of urban traffic scenarios lies in pose and orientation variations, slow or temporarily stopped vehicles and sudden illumination variations. To address these problems Sigma-Delta Mixture Model (SDMM)is proposed. Mixed distributions are updated dynamically based on matching and contribution in the two order temporal statistics. The constant amplification factor is replaced byweightedfactor to update the variance rate over its temporal activity. The proposed technique achieve robust and accurate performance,which improves adaptation capability with balanced sensitivity and reliability, moreover, integerlinear operations enables the real-time capability.en_US
dc.identifier.doi10.36478/jeasci.2016.414.419
dc.identifier.epage419
dc.identifier.issn1816-949X
dc.identifier.issue3
dc.identifier.spage414
dc.identifier.urihttps://medwelljournals.com/abstract/?doi=jeasci.2016.414.419
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/6681
dc.identifier.volume11
dc.language.isoen_USen_US
dc.publisherMedwell Journalsen_US
dc.relation.ispartofJournal of Engineering and Applied Sciencesen_US
dc.titleBackground Subtraction In Urban Traffic Video Using Recursive Sigma-delta Mixture Modelen_US
dc.typeArticleen_US
dspace.entity.typePublication

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