Publication detail

Realtime Pedestrian Recognition Using Siamese Network

RAJNOHA, M.

Original Title

Realtime Pedestrian Recognition Using Siamese Network

English Title

Realtime Pedestrian Recognition Using Siamese Network

Type

conference paper

Language

en

Original Abstract

Image similarity measuring has many various applications. Pedestrian recognition is one of them and for the security purposes it is basically required to run in real-time. This paper proposes a deep Siamese neural network architecture for pedestrian recognition that achieves 70.28% accuracy on the test set containing 20 persons. Prediction of the model is fast enough for real-time processing.

English abstract

Image similarity measuring has many various applications. Pedestrian recognition is one of them and for the security purposes it is basically required to run in real-time. This paper proposes a deep Siamese neural network architecture for pedestrian recognition that achieves 70.28% accuracy on the test set containing 20 persons. Prediction of the model is fast enough for real-time processing.

Keywords

surveillance, pedestrian, recognition, Siamese, deep learning

Released

26.04.2018

Publisher

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

Location

Brno

ISBN

978-80-214-5614-3

Book

Proceedings of the 24rd Conference STUDENT EEICT 2018

Edition number

první

Pages from

441

Pages to

445

Pages count

5

BibTex


@inproceedings{BUT147104,
  author="Martin {Rajnoha}",
  title="Realtime Pedestrian Recognition Using Siamese Network",
  annote="Image similarity measuring has many various applications. Pedestrian recognition is one of them and for the security purposes it is basically required to run in real-time. This paper proposes a deep Siamese neural network architecture for pedestrian recognition that achieves 70.28% accuracy on the test set containing 20 persons. Prediction of the model is fast enough for real-time processing.",
  address="Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií",
  booktitle="Proceedings of the 24rd Conference STUDENT EEICT 2018",
  chapter="147104",
  howpublished="online",
  institution="Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií",
  year="2018",
  month="april",
  pages="441--445",
  publisher="Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií",
  type="conference paper"
}