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Illegal parking detection using Gaussian mixture model and kalman filter

Akhawaji, R., SEDKY, Mohamed and SOLIMAN, Abdel-Hamid (2018) Illegal parking detection using Gaussian mixture model and kalman filter. Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA, 2017-O. pp. 840-847. ISSN 2161-5330

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Abstract or description

Automatic analysis of videos for traffic monitoring has been an area of significant research in the recent past. In this paper, we proposed a system to detect and track illegal vehicle parking using Gaussian Mixture Model and Kalman Filter. i-LIDS dataset is used to test and evaluate the algorithm by comparing the results with the ground truth provided, we have tested the system using 4 full videos from i-LIDS to detect parked vehicle whiten specific area. Region of interest has been used to detect Vehicle parks in a no parking zone over sixty seconds and remains stationary.Within the scope of this work, we highlighted the components of an automated traffic surveillance system, including background modeling, foreground extraction, Kalman filter and Gaussian mixture model. © 2017 IEEE.

Item Type: Article
Additional Information: “© © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works Presented at The Conference of 14th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2017 ; Conference Date: 30 October 2017 - 3 November 2017; Conference Code:135250
Faculty: School of Computing and Digital Technologies > Computing
Depositing User: Library STORE team
Date Deposited: 15 Jun 2018 10:42
Last Modified: 30 Jan 2019 10:03
URI: http://eprints.staffs.ac.uk/id/eprint/4418

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