ML Anthology

Vehicle Type Classification Using Bagging and Convolutional Neural Network on Multi View Surveillance Image

Pyong-Kun Kim, Kil-Taek Lim
CVPRW 2017 pp. 914-919
doi:10.1109/CVPRW.2017.126 /cvprw/2017/kim2017cvprw-vehicle/

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.

CVPRW Semantic Scholar

Cite

Text

Kim and Lim. "Vehicle Type Classification Using Bagging and Convolutional Neural Network on Multi View Surveillance Image." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017. doi:10.1109/CVPRW.2017.126

Markdown

[Kim and Lim. "Vehicle Type Classification Using Bagging and Convolutional Neural Network on Multi View Surveillance Image." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2017.](https://mlanthology.org/cvprw/2017/kim2017cvprw-vehicle/) doi:10.1109/CVPRW.2017.126

BibTeX

@inproceedings{kim2017cvprw-vehicle,
  title     = {{Vehicle Type Classification Using Bagging and Convolutional Neural Network on Multi View Surveillance Image}},
  author    = {Kim, Pyong-Kun and Lim, Kil-Taek},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2017},
  pages     = {914-919},
  doi       = {10.1109/CVPRW.2017.126},
  url       = {https://mlanthology.org/cvprw/2017/kim2017cvprw-vehicle/}
}