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Conference Paper
Vehicle Type Classification Using Bagging and Convolutional Neural Network on Multi View Surveillance Image
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- Authors
- Pyong-Kun Kim, Kil-Taek Lim
- Issue Date
- 2017-07
- Citation
- Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2017, pp.914-919
- Language
- English
- Type
- Conference Paper
- Abstract
- 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 Keywords
- 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