상세정보
학술대회
Vehicle Type Classification Using Bagging and Convolutional Neural Network on Multi View Surveillance Image
- 발행일
- 201707
- 출처
- Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2017, pp.914-919
- 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