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논문 검색
구분 SCI
연도 ~ 키워드

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학술대회 Vehicle Type Classification Using Bagging and Convolutional Neural Network on Multi View Surveillance Image
Cited 38 time in scopus Download 7 time Share share facebook twitter linkedin kakaostory
저자
김병근, 임길택
발행일
201707
출처
Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2017, pp.914-919
DOI
https://dx.doi.org/10.1109/CVPRW.2017.126
협약과제
17ZD1100, 대경권 지역산업연계 IT융합기술개발 및 산업계 지원사업, 문기영
초록
This paper aims to introduce a new vehicle type classification scheme on the images from multi-view surveillance camera. We propose four concepts to increase the performance on the images which have various resolutions from multi-view point. The Deep Learning method is essential to multi-view point image, bagging method makes system robust, data augmentation help to grow the classification capability, and post-processing compensate for imbalanced data. We combine these schemes and build a novel vehicle type classification system. Our system shows 97.84% classification accuracy on the 103,833 images in classification challenge dataset.
KSP 제안 키워드
Classification scheme, Classification system, Convolution neural network(CNN), Data Augmentation, Deep learning method, Multi-view, Post-Processing, View Point, classification accuracy, deep learning(DL), imbalanced data