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SOCHOR, J. HEROUT, A. HAVEL, J.
Original Title
BoxCars: 3D Boxes as CNN Input for Improved Fine-Grained Vehicle Recognition
Type
conference paper
Language
English
Original Abstract
We are dealing with the problem of fine-grained vehicle make&model recognition and verification. Our contribution is showing that extracting additional data from the video stream - besides the vehicle image itself - and feeding it into the deep convolutional neural network boosts the recognition performance considerably. This additional information includes: 3D vehicle bounding box used for "unpacking" the vehicle image, its rasterized low-resolution shape, and information about the 3D vehicle orientation. Experiments show that adding such information decreases classification error by 26% (the accuracy is improved from 0.772 to 0.832) and boosts verification average precision by 208% (0.378 to 0.785) compared to baseline pure CNN without any input modifications. Also, the pure baseline CNN outperforms the recent state of the art solution by 0.081. We provide an annotated set "BoxCars" of surveillance vehicle images augmented by various automatically extracted auxiliary information. Our approach and the dataset can considerably improve the performance of traffic surveillance systems.
Keywords
Fine-grained recognition, vehicles, CNN, input modification
Authors
SOCHOR, J.; HEROUT, A.; HAVEL, J.
Released
10. 3. 2016
Publisher
IEEE Computer Society
Location
Las Vegas
ISBN
978-1-4673-8851-1
Book
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
1063-6919
Periodical
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Number
6
State
unknown
Pages from
3006
Pages to
3015
Pages count
10
URL
http://ieeexplore.ieee.org/document/7780697/
BibTex
@inproceedings{BUT130949, author="Jakub {Sochor} and Adam {Herout} and Jiří {Havel}", title="BoxCars: 3D Boxes as CNN Input for Improved Fine-Grained Vehicle Recognition", booktitle="The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)", year="2016", journal="Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition", number="6", pages="3006--3015", publisher="IEEE Computer Society", address="Las Vegas", doi="10.1109/CVPR.2016.328", isbn="978-1-4673-8851-1", issn="1063-6919", url="http://ieeexplore.ieee.org/document/7780697/" }