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Proceedings Paper

Vehicle detection based on the use of shadow region and edge
Author(s): Sangheon Jeong; Seongkoo Kang; Joongkyu Kim
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Paper Abstract

Automotive and advanced driver assistance systems have attracted a great deal of attention lately. In these systems, effective and reliable vehicle detection is important because such systems can reduce the number of accidents and save human’ lives. This paper describes an approach to detecting a forward vehicle using a camera mounted on the moving vehicle. In this paper, we describe two methods to detect a vehicle on the road. First, by using the vehicle’s shadow, we can obtain the general location of the vehicular candidate. Second, we can identify the strong vertical edges at the left and right position of a vehicle. By combining the shadows and the edge, we can detect the vehicle’s location. But other regions may also be detected, such as car windows, reflections, and illumination by the sun. In order to remove these other factors, defined as noises, we need to use a filter. After using the filter, we can calculate the exact location of the vehicle. Additionally, by using connected component labeling, we can obtain coordinates and establish the vehicle’s location. Connected component labeling find all connected components in an image and assigns a unique label to all points in the same component. These methods are very useful for vehicle detection and the development of the driving assistance systems, and they can protect drivers’ safety from having an accident.

Paper Details

Date Published: 19 July 2013
PDF: 4 pages
Proc. SPIE 8878, Fifth International Conference on Digital Image Processing (ICDIP 2013), 88782Y (19 July 2013); doi: 10.1117/12.2030790
Show Author Affiliations
Sangheon Jeong, Sungkyunkwan Univ. (Korea, Republic of)
Seongkoo Kang, Sungkyunkwan Univ. (Korea, Republic of)
Joongkyu Kim, Sungkyunkwan Univ. (Korea, Republic of)

Published in SPIE Proceedings Vol. 8878:
Fifth International Conference on Digital Image Processing (ICDIP 2013)
Yulin Wang; Xie Yi, Editor(s)

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