Detail publikace

Exploiting neighbors for faster scanning window detection in images

ZEMČÍK, P. HRADIŠ, M. HEROUT, A.

Originální název

Exploiting neighbors for faster scanning window detection in images

Typ

článek ve sborníku mimo WoS a Scopus

Jazyk

angličtina

Originální abstrakt

Detection of objects through scanning windows is widely used and accepted method. The detectors traditionally do not make use of information that is shared between neighboring image positions although this fact means that the traditional solutions are not optimal. Addressing this, we propose an efficient and computationally inexpensive approach how to exploit the shared information and thus increase speed of detection. The main idea is to predict responses of the classifier in neighbor windows close to the ones already evaluated and skip such positions where the prediction is confident enough. In order to predict the responses, the proposed algorithm builds a new classifier which reuses the set of image features already exploited. The results show that the proposed approach can reduce scanning time up to four times with only minor increase of error rate. On the presented examples it is shown that, it is possible to reach less than one feature computed on average per single image position. The paper presents the algorithm itself and also results of experiments on several data sets and with different types of image features.

Klíčová slova

real-time object detection, image features, WaldBoost

Autoři

ZEMČÍK, P.; HRADIŠ, M.; HEROUT, A.

Rok RIV

2010

Vydáno

13. 12. 2010

Nakladatel

Springer Verlag

Místo

Sydney

ISBN

978-3-642-17690-6

Kniha

ACIVS 2010

Edice

Lecture Notes in Computer Science

Strany od

100

Strany do

111

Strany počet

12

URL

BibTex

@inproceedings{BUT35132,
  author="Pavel {Zemčík} and Michal {Hradiš} and Adam {Herout}",
  title="Exploiting neighbors for faster scanning window detection in images",
  booktitle="ACIVS 2010",
  year="2010",
  series="Lecture Notes in Computer Science",
  volume="6475",
  pages="100--111",
  publisher="Springer Verlag",
  address="Sydney",
  isbn="978-3-642-17690-6",
  url="https://www.fit.vut.cz/research/publication/9410/"
}