Processing multiple image streams for real-time monitoring of parking lots

Liu, Yu-Hsn, Ong, Kok-Leong, Lee, Vincent C. S. and Chen, Yi-Ping Phoebe 2012, Processing multiple image streams for real-time monitoring of parking lots, International journal on computer, consumer and control, vol. 1, no. 1, pp. 23-32.

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Title Processing multiple image streams for real-time monitoring of parking lots
Author(s) Liu, Yu-Hsn
Ong, Kok-Leong
Lee, Vincent C. S.
Chen, Yi-Ping Phoebe
Journal name International journal on computer, consumer and control
Volume number 1
Issue number 1
Start page 23
End page 32
Total pages 10
Publisher National Chin-Yi University of Technology
Place of publication Taichung, Taiwan
Publication date 2012-01-10
ISSN 2304-2516
Summary We present a system to detect parked vehicles in a typical parking complex using multiple streams of images captured through IP connected devices. Compared to traditional object detection techniques and machine learning methods, our approach is significantly faster in detection speed in the presence of multiple image streams. It is also capable of comparable accuracy when put to test against existing methods. And this is achieved without the need to train the system that machine learning methods require. Our approach uses a combination of psychological insights obtained from human detection and an algorithm replicating the outcomes of a SVM learner but without the noise that compromises accuracy in the normal learning process. Performance enhancements are made on the algorithm so that it operates well in the context of multiple image streams. The result is faster detection with comparable accuracy. Our experiments on images captured from a local test site shows very promising results for an implementation that is not only effective and low cost but also opens doors to new parking applications when combined with other technologies.
Language eng
Field of Research 080106 Image Processing
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category C1 Refereed article in a scholarly journal
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Document type: Journal Article
Collection: School of Information Technology
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