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Proceedings Paper

Robust component-based car detection and counting in aerial imagery based on the mean-shift colour space clustering
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Paper Abstract

In the aerial images of 11.2 cm/pixel resolution the car components that can be seen are only large parts of the car such as car bodies, windshields, doors and shadows. Furthermore, these components are distorted by low spatial resolution, low color contrast, specular reflection and viewpoint variation. We use the mean shift procedure for robust segmentation of the car parts in the geometric and color joint space. This approach is robust, efficient, repeatable and independent of the threshold parameters. We introduce a hierarchical segmentation algorithm with three consecutive mean-shift procedures. Each is designed with a specific bandwidth to segment a specific car part, whose size is estimated a priori, and is followed by a support vector machine in order to detect this car part, based on the color features and the geometrical moment based features. The procedure starts with the largest car parts, which are then removed from the segmented region lists after the detection to avoid over-segmentation of large regions with the mean-shift using smaller bandwidth values. Finally we detect and count the cars in the image by combining the detected car parts according to the spatial relations. Experiment results show a good performance.

Paper Details

Date Published: 26 April 2010
PDF: 7 pages
Proc. SPIE 7668, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications VII, 76680H (26 April 2010); doi: 10.1117/12.852613
Show Author Affiliations
Yueh Ouyang, Univ. Laval (Canada)
Samir Sahli, Univ. Laval (Canada)
Yunlong Sheng, Univ. Laval (Canada)
Daniel A. Lavigne, Defence Research and Development Canada (Canada)


Published in SPIE Proceedings Vol. 7668:
Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications VII
Daniel J. Henry, Editor(s)

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