Authors: Yongbin Gao; Hyo Jong Lee
Addresses: Division of Computer Science and Engineering, Chonbuk National University, 567 Baekje-Daero, Deokjin-Gu, Jeonju 54596, Republic of Korea ' Division of Computer Science and Engineering, Center for Advanced Image and Information Technology, Chonbuk National University, 567 Baekje-Daero, Deokjin-Gu, Jeonju 54596, Republic of Korea
Abstract: Vehicle analysis involves licence plate recognition, vehicle type recognition, and car manufacturer and model recognition. Car manufacturer and model recognition plays an important role in providing supplementary information to licence plate recognition for the unique identification of a car. In this paper, we propose a framework to recognition car manufacturer and its model based on scale invariant feature transform (SIFT). We first detect a moving car using frame differences; the resultant binary image is used to detect the frontal view of a car by a symmetry filter. The detected frontal view is then used to identify a car based on SIFT algorithm. Experimental results show that our proposed framework achieves favourable recognition accuracy.
Keywords: moving car detection; car model recognition; scale invariant feature transform; SIFT.
International Journal of Computational Vision and Robotics, 2018 Vol.8 No.1, pp.32 - 41
Received: 03 Sep 2015
Accepted: 08 Feb 2016
Published online: 27 Feb 2018 *