Local Tiled Deep Networks for Recognition of Vehicle Make and Model
AbstractVehicle 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
Scifeed alert for new publicationsNever 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
Gao, Y.; Lee, H.J. Local Tiled Deep Networks for Recognition of Vehicle Make and Model. Sensors 2016, 16, 226.
Gao Y, Lee HJ. Local Tiled Deep Networks for Recognition of Vehicle Make and Model. Sensors. 2016; 16(2):226.Chicago/Turabian Style
Gao, Yongbin; Lee, Hyo J. 2016. "Local Tiled Deep Networks for Recognition of Vehicle Make and Model." Sensors 16, no. 2: 226.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.