Publication detail

Depth-Based Filtration for Tracking Boost

CHRÁPEK, D. BERAN, V. ZEMČÍK, P.

Original Title

Depth-Based Filtration for Tracking Boost

Type

conference paper

Language

English

Original Abstract

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.

Keywords

Real-time, RGBD, Segmentation, Tracking

Authors

CHRÁPEK, D.; BERAN, V.; ZEMČÍK, P.

RIV year

2015

Released

6. 11. 2015

Publisher

Springer International Publishing

Location

Catania

ISBN

978-3-319-25903-1

Book

Springer International Publishing

Edition

Lecture Notes in Computer Science

ISBN

0302-9743

Periodical

Lecture Notes in Computer Science

Year of study

9386

Number

9386

State

Federal Republic of Germany

Pages from

217

Pages to

228

Pages count

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"
}