Vehicle Type Classification Using Bagging and Convolutional Neural Network on Multi View Surveillance Image
Pyong-Kun Kim, Kil-Taek Lim · 2017
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.