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

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: Aug 25, 2022
• 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