Detail publikace

BoxCars: 3D Boxes for Improved Fine-Grained Vehicle Recognition

SOCHOR, J. ŠPAŇHEL, J. HEROUT, A.

Originální název

BoxCars: 3D Boxes for Improved Fine-Grained Vehicle Recognition

Typ

abstrakt

Jazyk

angličtina

Originální abstrakt

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. This extended abstract is based on previously published CVPR paper and extended journal version of the paper which is currently under review.

Autoři

SOCHOR, J.; ŠPAŇHEL, J.; HEROUT, A.

Vydáno

19. 7. 2017

Nakladatel

IEEE Computer Society

Místo

Honolulu, HI

Strany od

1

Strany do

2

Strany počet

2

BibTex

@misc{BUT168564,
  author="Jakub {Sochor} and Jakub {Špaňhel} and Adam {Herout}",
  title="BoxCars: 3D Boxes for Improved Fine-Grained Vehicle Recognition",
  booktitle="The Fourth Workshop on Fine-Grained Visual Categorization (CVPR 2017)",
  year="2017",
  pages="1--2",
  publisher="IEEE Computer Society",
  address="Honolulu, HI",
  note="abstract"
}