Publication detail

On Possibilities of Human Head Detection for Person Flow Monitoring System

Dolezel P. Stursa D. Skrabanek P.

Original Title

On Possibilities of Human Head Detection for Person Flow Monitoring System

Type

conference paper

Language

English

Original Abstract

Along with the development of human society, economy, industry and engineering, as well as with growing population in the world’s biggest cities, various approaches to person detection have become the subject of great interest. One approach to developing a person detection system is proposed in this paper. A high-angle video sequence is considered as the input to the system. Then, three classification algorithms are considered: support vector machines, pattern recognition neural networks and convolutional neural networks. The results showed very little difference between the classifiers, with the overall accuracy more than 95% over a testing set.

Keywords

Person flow monitoring, Support vector machines, Pattern recognition neural network, Convolutional neural network. Histograms of oriented gradients

Authors

Dolezel P.; Stursa D.; Skrabanek P.

Released

16. 5. 2019

Publisher

Springer Verlag

Location

Cham

ISBN

0302-9743

Periodical

Lecture Notes in Computer Science

Year of study

11507 LNCS

State

Federal Republic of Germany

Pages from

402

Pages to

413

Pages count

12

URL

https://link.springer.com/chapter/10.1007/978-3-030-20518-8_34

BibTex

@inproceedings{BUT157763,
  author="Dolezel P. and Stursa D. and Skrabanek P.",
  title="On Possibilities of Human Head Detection for Person Flow Monitoring System",
  booktitle="Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  year="2019",
  journal="Lecture Notes in Computer Science",
  volume="11507 LNCS",
  pages="402--413",
  publisher="Springer Verlag",
  address="Cham",
  doi="10.1007/978-3-030-20518-8\{_}34",
  issn="0302-9743",
  url="https://link.springer.com/chapter/10.1007/978-3-030-20518-8_34"
}