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Robust Vehicle Detection under Various Environmental Conditions Using an Infrared Thermal Camera and Its Application to Road Traffic Flow Monitoring

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      Abstract

      We have already proposed a method for detecting vehicle positions and their movements (henceforth referred to as “our previous method”) using thermal images taken with an infrared thermal camera. Our experiments have shown that our previous method detects vehicles robustly under four different environmental conditions which involve poor visibility conditions in snow and thick fog. Our previous method uses the windshield and its surroundings as the target of the Viola-Jones detector. Some experiments in winter show that the vehicle detection accuracy decreases because the temperatures of many windshields approximate those of the exterior of the windshields. In this paper, we propose a new vehicle detection method (henceforth referred to as “our new method”). Our new method detects vehicles based on tires' thermal energy reflection. We have done experiments using three series of thermal images for which the vehicle detection accuracies of our previous method are low. Our new method detects 1,417 vehicles (92.8%) out of 1,527 vehicles, and the number of false detection is 52 in total. Therefore, by combining our two methods, high vehicle detection accuracies are maintained under various environmental conditions. Finally, we apply the traffic information obtained by our two methods to traffic flow automatic monitoring, and show the effectiveness of our proposal.

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      Most cited references 28

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      On-road vehicle detection: a review.

      Developing on-board automotive driver assistance systems aiming to alert drivers about driving environments, and possible collision with other vehicles has attracted a lot of attention lately. In these systems, robust and reliable vehicle detection is a critical step. This paper presents a review of recent vision-based on-road vehicle detection systems. Our focus is on systems where the camera is mounted on the vehicle rather than being fixed such as in traffic/driveway monitoring systems. First, we discuss the problem of on-road vehicle detection using optical sensors followed by a brief review of intelligent vehicle research worldwide. Then, we discuss active and passive sensors to set the stage for vision-based vehicle detection. Methods aiming to quickly hypothesize the location of vehicles in an image as well as to verify the hypothesized locations are reviewed next. Integrating detection with tracking is also reviewed to illustrate the benefits of exploiting temporal continuity for vehicle detection. Finally, we present a critical overview of the methods discussed, we assess their potential for future deployment, and we present directions for future research.
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        Rapid object detection using a boosted cascade of simple features

         P Viola,  M. JONES,  M Jones (2001)
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          Automotive Radar—Investigation of Mutual Interference Mechanisms

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            Author and article information

            Affiliations
            Department of Information Engineering, Graduate School of Industrial Engineering, Tokai University, 9-1-1 Toroku, Higashi-ku, Kumamoto 862-8652, Japan; E-Mails: 2bijm001@123456mail.tokai-u.jp (M.M.); nakamiya@123456tokai.ac.jp (T.N.)
            Author notes
            [*] Author to whom correspondence should be addressed; E-Mail: iwasaki@123456tokai.ac.jp; Tel.: +81-96-386-2717; Fax: +81-96-381-7956.
            Journal
            Sensors (Basel)
            Sensors (Basel)
            Sensors (Basel, Switzerland)
            Molecular Diversity Preservation International (MDPI)
            1424-8220
            June 2013
            17 June 2013
            : 13
            : 6
            : 7756-7773
            © 2013 by the authors; licensee MDPI, Basel, Switzerland.

            This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).

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