Performance analysis and learning approaches for vehicle detection and counting in aerial images

V. Parameswaran, Philippe Burlina, R. Chellappa

Research output: Contribution to journalConference article

Abstract

Robustness as well as the ability to work in an unsupervised mode are two desirable features of algorithms employed on large image databases. This paper describes parameter optimization strategies for such algorithms and motivates these strategies by focussing on aerial image exploitation and studying certain specific aerial image understanding algorithms, namely local vehicle detection and global vehicle configuration detection. The paper first gives a brief introduction to the problem in the context of aerial imagery. Next, a high level description of the algorithms and parameters that need to be optimized is given. Strategies for parameter optimization are illustrated using examples. Finally a discussion on the applicability and scope for improvement of the strategies is given.

Original languageEnglish (US)
Pages (from-to)2753-2756
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume4
StatePublished - Jan 1 1997
Externally publishedYes
EventProceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 5) - Munich, Ger
Duration: Apr 21 1997Apr 24 1997

Fingerprint

Antennas
Image understanding

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Performance analysis and learning approaches for vehicle detection and counting in aerial images. / Parameswaran, V.; Burlina, Philippe; Chellappa, R.

In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Vol. 4, 01.01.1997, p. 2753-2756.

Research output: Contribution to journalConference article

@article{6b6c37b8b4ad4bfd94f64fccf70684c7,
title = "Performance analysis and learning approaches for vehicle detection and counting in aerial images",
abstract = "Robustness as well as the ability to work in an unsupervised mode are two desirable features of algorithms employed on large image databases. This paper describes parameter optimization strategies for such algorithms and motivates these strategies by focussing on aerial image exploitation and studying certain specific aerial image understanding algorithms, namely local vehicle detection and global vehicle configuration detection. The paper first gives a brief introduction to the problem in the context of aerial imagery. Next, a high level description of the algorithms and parameters that need to be optimized is given. Strategies for parameter optimization are illustrated using examples. Finally a discussion on the applicability and scope for improvement of the strategies is given.",
author = "V. Parameswaran and Philippe Burlina and R. Chellappa",
year = "1997",
month = "1",
day = "1",
language = "English (US)",
volume = "4",
pages = "2753--2756",
journal = "Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing",
issn = "0736-7791",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Performance analysis and learning approaches for vehicle detection and counting in aerial images

AU - Parameswaran, V.

AU - Burlina, Philippe

AU - Chellappa, R.

PY - 1997/1/1

Y1 - 1997/1/1

N2 - Robustness as well as the ability to work in an unsupervised mode are two desirable features of algorithms employed on large image databases. This paper describes parameter optimization strategies for such algorithms and motivates these strategies by focussing on aerial image exploitation and studying certain specific aerial image understanding algorithms, namely local vehicle detection and global vehicle configuration detection. The paper first gives a brief introduction to the problem in the context of aerial imagery. Next, a high level description of the algorithms and parameters that need to be optimized is given. Strategies for parameter optimization are illustrated using examples. Finally a discussion on the applicability and scope for improvement of the strategies is given.

AB - Robustness as well as the ability to work in an unsupervised mode are two desirable features of algorithms employed on large image databases. This paper describes parameter optimization strategies for such algorithms and motivates these strategies by focussing on aerial image exploitation and studying certain specific aerial image understanding algorithms, namely local vehicle detection and global vehicle configuration detection. The paper first gives a brief introduction to the problem in the context of aerial imagery. Next, a high level description of the algorithms and parameters that need to be optimized is given. Strategies for parameter optimization are illustrated using examples. Finally a discussion on the applicability and scope for improvement of the strategies is given.

UR - http://www.scopus.com/inward/record.url?scp=0030649055&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0030649055&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:0030649055

VL - 4

SP - 2753

EP - 2756

JO - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing

JF - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing

SN - 0736-7791

ER -