Next Article in Journal
Removing the Interdependency between Horizontal and Vertical Eye-Movement Components in Electrooculograms
Next Article in Special Issue
Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems †
Previous Article in Journal
A Unified Global Reference Frame of Vertical Crustal Movements by Satellite Laser Ranging
Previous Article in Special Issue
Mobility-Enhanced Reliable Geographical Forwarding in Cognitive Radio Sensor Networks
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(2), 226; doi:10.3390/s16020226

Local Tiled Deep Networks for Recognition of Vehicle Make and Model

Division of Computer Science and Engineering, Chonbuk National University, 567 Baekje-Daero, Deokjin-Gu, Jeonju 54596, Korea
Center for Advanced Image and Information Technology, Chonbuk National University, 567 Baekje-Daero, Deokjin-Gu, Jeonju 54596, Korea
Author to whom correspondence should be addressed.
Received: 5 January 2016 / Revised: 2 February 2016 / Accepted: 5 February 2016 / Published: 11 February 2016
(This article belongs to the Special Issue Mobile Sensor Computing: Theory and Applications)
View Full-Text   |   Download PDF [1924 KB, uploaded 16 February 2016]   |  


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. View Full-Text
Keywords: moving-vehicle detection; vehicle-model recognition; deep learning; HOG. moving-vehicle detection; vehicle-model recognition; deep learning; HOG.

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Gao, Y.; Lee, H.J. Local Tiled Deep Networks for Recognition of Vehicle Make and Model. Sensors 2016, 16, 226.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top