Citation

Yeo, Boon Chin and Lim, Heng Siong and Lim, Way Soong (2016) Vehicle detection for thermal vision-based traffic monitoring system using principal component analysis. International Journal of Innovative Computing, Information and Control (IJICIC), 12 (5). p. 1467. ISSN 1349-418X

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Abstract

Machine vision is a popular technology used in Traffic Monitoring System (TMS) to detect vehicles in the traffic scene. Recently, thermal vision provides an alternative machine vision for the TMS since it demonstrates good vehicle detection accuracy, especially under night condition. Under the thermal vision, the vehicles appear almost similar in the daytime and nighttime, even though the illuminations of the traffic scene are significantly different. This vision effect motivates the development of a single vehicle detection algorithm that works in both of the illumination conditions. It is a challenge that has existed for decades. In this paper, a framework for thermal-vision-based TMS is proposed. Histogram of Oriented Gradients (HoG) is a feature descriptor used to recognize the vehicles on the road. The similar appearance of the vehicles under the thermal vision allows the use of single adaptive reference descriptor in vehicle detection for each lane of the road, in which the reference descriptor is generated and optimized with Principal Component Analysis (PCA). In both the daytime and nighttime, the proposed TMS framework has demonstrated high vehicle detection accuracies under the thermal vision.

Item Type:	Article
Uncontrolled Keywords:	Vehicle detection, Traffic monitoring, Thermal vision, Principal component analysis, Histogram of oriented gradients
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	20 Feb 2017 04:31
Last Modified:	20 Feb 2017 04:32
URII:	http://shdl.mmu.edu.my/id/eprint/6457

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