Detail publikace

Depth-Based Filtration for Tracking Boost

CHRÁPEK, D. BERAN, V. ZEMČÍK, P.

Originální název

Depth-Based Filtration for Tracking Boost

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

This paper presents a novel depth information utilization method for performance boosting of tracking in traditional RGB trackers for arbitrary objects (objects not known in advance) by object segmentation/separation supported by depth information. The main focus is on real-time applications, such as robotics or surveillance, where exploitation of depth sensors, that are nowadays affordable, is not only possible but also feasible. The aim is to show that the depth information used for target segmentation significantly helps reducing incorrect model updates caused by occlusion or drifts and improves success rate and precision of traditional RGB tracker while keeping comparably efficient and thus possibly real-time. The paper also presents and discusses the achieved performance results.

Klíčová slova

Real-time, RGBD, Segmentation, Tracking

Autoři

CHRÁPEK, D.; BERAN, V.; ZEMČÍK, P.

Rok RIV

2015

Vydáno

6. 11. 2015

Nakladatel

Springer International Publishing

Místo

Catania

ISBN

978-3-319-25903-1

Kniha

Springer International Publishing

Edice

Lecture Notes in Computer Science

ISSN

0302-9743

Periodikum

Lecture Notes in Computer Science

Ročník

9386

Číslo

9386

Stát

Spolková republika Německo

Strany od

217

Strany do

228

Strany počet

12

URL

http://link.springer.com/chapter/10.1007%2F978-3-319-25903-1_19

BibTex

@inproceedings{BUT119922,
  author="David {Chrápek} and Vítězslav {Beran} and Pavel {Zemčík}",
  title="Depth-Based Filtration for Tracking Boost",
  booktitle="Springer International Publishing",
  year="2015",
  series="Lecture Notes in Computer Science",
  journal="Lecture Notes in Computer Science",
  volume="9386",
  number="9386",
  pages="217--228",
  publisher="Springer International Publishing",
  address="Catania",
  doi="10.1007/978-3-319-25903-1\{_}19",
  isbn="978-3-319-25903-1",
  issn="0302-9743",
  url="http://link.springer.com/chapter/10.1007%2F978-3-319-25903-1_19"
}