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


  1. Ouyang, Y.
  2. Sahli, S.
  3. Sheng, Y.
  4. Lavigne, D.A.
Corporate Authors
Defence R&D Canada - Valcartier, Valcartier QUE (CAN);Laval Univ, Quebec QUE (CAN)
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.
Report Number
DRDC-VALCARTIER-SL-2010-490 — Scientific Literature
Date of publication
01 Apr 2010
Number of Pages
Reprinted from
Airborne Ingelligence, Surveillance (ISR) Systems and Applications VII, vol 7668, 2010
Electronic Document(PDF)

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