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학술대회 Deep Learning-Based Vehicle Classification Using an Ensemble of Local Expert and Global Networks
Cited 20 time in scopus Download 4 time Share share facebook twitter linkedin kakaostory
저자
이종택, 정윤수
발행일
201706
출처
Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2017, pp.920-925
DOI
https://dx.doi.org/10.1109/CVPRW.2017.127
협약과제
17ZD1100, 대경권 지역산업연계 IT융합기술개발 및 산업계 지원사업, 문기영
초록
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 제안 키워드
Global Network, High performance, Illumination change, Learning-based, Vehicle classification, deep learning(DL), large-scale, learning algorithms, pose variation, traffic surveillance