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Conference Paper Deep Learning-Based Vehicle Classification Using an Ensemble of Local Expert and Global Networks
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Authors
Jong Taek Lee, Yunsu Chung
Issue Date
2017-06
Citation
Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2017, pp.920-925
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/CVPRW.2017.127
Project Code
17ZD1100, (Daegu-Gyeongbuk)Regional Industry IT Convergence Technology Development and Support Project , Moon Ki Young
Abstract
Vehicle classification has been a challenging problem because of pose variations, weather / illumination changes, inter-class similarity and insufficient training dataset. With the help of innovative deep learning algorithms and large scale traffic surveillance dataset, we are able to achieve high performance on vehicle classification. In order to improve performance, we propose an ensemble of global networks and mixture of K local expert networks. It achieved a mean accuracy of 97.92%, a mean precision of 92.98%, a mean recall of 90.24% and a Cohen Kappa score of 96.75% on unseen test dataset from the MIO-TCD classification challenge.
KSP Keywords
Global Network, High performance, Illumination change, Learning-based, Vehicle classification, deep learning(DL), large-scale, learning algorithms, pose variation, traffic surveillance