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Detection and recognition of illegally parked vehicles based on an adaptive gaussian mixture model and a seed fill algorithm.

Sarker, Md. Mostafa Kamal; Weihua, Cai; Song, Moon Kyou

Authors

Md. Mostafa Kamal Sarker

Cai Weihua

Moon Kyou Song



Abstract

In this paper, we present an algorithm for the detection of illegally parked vehicles based on a combination of some image processing algorithms. A digital camera is fixed in the illegal parking region to capture the video frames. An adaptive Gaussian mixture model (GMM) is used for background subtraction in a complex environment to identify the regions of moving objects in our test video. Stationary objects are detected by using the pixel-level features in time sequences. A stationary vehicle is detected by using the local features of the object, and thus, information about illegally parked vehicles is successfully obtained. An automatic alarm system can be utilized according to the different regulations of different illegal parking regions. The results of this study obtained using a test video sequence of a real-time traffic scene show that the proposed method is effective.

Citation

SARKER, M.M.K., WEIHUA, C. and SONG, M.K. 2015. Detection and recognition of illegally parked vehicles based on an adaptive gaussian mixture model and a seed fill algorithm. Journal of information and communication convergence engineering [online], 13(3), pages 197-204. Available from: https://doi.org/10.6109/jicce.2015.13.3.197

Journal Article Type Article
Acceptance Date Jul 10, 2015
Online Publication Date Aug 28, 2015
Publication Date Sep 30, 2015
Deposit Date Dec 4, 2021
Publicly Available Date Mar 28, 2024
Journal Journal of information and communication convergence engineering
Print ISSN 2234-8255
Electronic ISSN 2234-8883(
Publisher Korea Institute of Information and Communication Engineering (KIICE)
Peer Reviewed Peer Reviewed
Volume 13
Issue 3
Pages 197-204
DOI https://doi.org/10.6109/jicce.2015.13.3.197
Keywords Background subtraction; Illegally parked vehicle; Local features; Seed fill algorithm; Traffic surveillance
Public URL https://rgu-repository.worktribe.com/output/1542184

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