title: Local Tiled Deep Networks for Recognition of Vehicle Make and Model.
abstract: Vehicle analysis involves license-plate recognition (LPR), vehicle-type classification (VTC), and vehicle make and model recognition (MMR). Among these tasks, MMR plays an important complementary role in respect to LPR. In this paper, we propose a novel framework for MMR using local tiled deep networks. The frontal views of vehicle images are first extracted and fed into the local tiled deep networks for training and testing. A local tiled convolutional neural network (LTCNN) is proposed to alter the weight sharing scheme of CNN with local tiled structure. The LTCNN unties the weights of adjacent units and then ties the units k steps from each other within a local map. This architecture provides the translational, rotational, and scale invariance as well as locality. In addition, to further deal with the colour and illumination variation, we applied the histogram oriented gradient (HOG) to the frontal view of images prior to the LTCNN. The experimental results show that our LTCNN framework achieved a 98% accuracy rate in terms of vehicle MMR.
authors.affiliation: Division of Computer Science and Engineering, Chonbuk National University, 567 Baekje-Daero, Deokjin-Gu, Jeonju 54596, Korea. gaoyongbin.sam@gmail.com.
authors.forename: Yongbin
authors.initials: Y
authors.lastname: Gao
authors.affiliation: Division of Computer Science and Engineering, Chonbuk National University, 567 Baekje-Daero, Deokjin-Gu, Jeonju 54596, Korea. hlee@chonbuk.ac.kr.|Center for Advanced Image and Information Technology, Chonbuk National University, 567 Baekje-Daero, Deokjin-Gu, Jeonju 54596, Korea. hlee@chonbuk.ac.kr.
authors.forename: Hyo Jong
authors.initials: HJ
authors.lastname: Lee
keywords: HOG.; deep learning; moving-vehicle detection; vehicle-model recognition