Detail publikačního výsledku

Realtime Pedestrian Recognition Using Siamese Network

RAJNOHA, M.

Originální název

Realtime Pedestrian Recognition Using Siamese Network

Anglický název

Realtime Pedestrian Recognition Using Siamese Network

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

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.

Anglický abstrakt

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.

Klíčová slova

surveillance, pedestrian, recognition, Siamese, deep learning

Klíčová slova v angličtině

surveillance, pedestrian, recognition, Siamese, deep learning

Autoři

RAJNOHA, M.

Rok RIV

2019

Vydáno

26.04.2018

Nakladatel

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

Místo

Brno

ISBN

978-80-214-5614-3

Kniha

Proceedings of the 24rd Conference STUDENT EEICT 2018

Strany od

441

Strany do

445

Strany počet

5

BibTex

@inproceedings{BUT147104,
  author="Martin {Rajnoha}",
  title="Realtime Pedestrian Recognition Using Siamese Network",
  booktitle="Proceedings of the 24rd Conference STUDENT EEICT 2018",
  year="2018",
  number="první",
  pages="441--445",
  publisher="Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií",
  address="Brno",
  isbn="978-80-214-5614-3"
}