Conference paper

SOCHOR Jakub, HEROUT Adam and HAVEL Jiří. BoxCars: 3D Boxes as CNN Input for Improved Fine-Grained Vehicle Recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE Computer Society, 2016, pp. 3006-3015. ISBN 978-1-4673-8851-1. ISSN 1063-6919. Available from: http://ieeexplore.ieee.org/document/7780697/
Publication language:english
Original title:BoxCars: 3D Boxes as CNN Input for Improved Fine-Grained Vehicle Recognition
Title (cs):BoxCars: 3D Boxes jako vstup pro CNN zlepšující fine-grained klasifikaci automobilů
Pages:3006-3015
Proceedings:The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Conference:Computer Vision and Pattern Recognition 2016
Place:Las Vegas, US
Year:2016
URL:http://ieeexplore.ieee.org/document/7780697/
ISBN:978-1-4673-8851-1
Journal:Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, No. 6, US
ISSN:1063-6919
Publisher:IEEE Computer Society
Keywords
Fine-grained recognition, vehicles, CNN, input modification
Annotation
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.
BibTeX:
@INPROCEEDINGS{
   author = {Jakub Sochor and Adam Herout and Ji{\v{r}}{\'{i}} Havel},
   title = {BoxCars: 3D Boxes as CNN Input for Improved Fine-Grained
	Vehicle Recognition},
   pages = {3006--3015},
   booktitle = {The IEEE Conference on Computer Vision and Pattern
	Recognition (CVPR)},
   journal = {Proceedings of the IEEE Computer Society Conference on
	Computer Vision and Pattern Recognition},
   number = {6},
   year = {2016},
   location = {Las Vegas, US},
   publisher = {IEEE Computer Society},
   ISBN = {978-1-4673-8851-1},
   ISSN = {1063-6919},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php.en.iso-8859-2?id=11103}
}

Your IPv4 address: 54.162.154.91
Switch to IPv6 connection

DNSSEC [dnssec]