Human detection and tracking through temporal feature recognition

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

3 Citations (Scopus)
29 Downloads (Pure)

Abstract

The ability to accurately track objects of interest – particularly humans – is of great importance in the fields of security and surveillance. In such scenarios, t he application of accurate, automated human tracking offers benefits over manual supervision. In this paper, recent efforts made to investigate the improvement of automated human detection and tracking techniques through the recognition
of person-specific time-varying signatures in thermal video are detailed. A robust human detection algorithm is developed to aid the initialisation stage of a state-of-the art existing tracking algorithm. In addition, coupled with the spatial tracking methods present in this algorithm, the inclusion of temporal signature recognition in the tracking process is shown to improve human tracking results.
Original languageEnglish
Title of host publication2014 22nd European Signal Processing Conference (EUSIPCO)
PublisherIEEE
Pages2180-2184
Number of pages5
ISBN (Print)9780992862619
Publication statusPublished - 1 Sep 2014
Event22nd European Signal Processing Conference - Lisbon Congress Centre, Lisbon, Portugal
Duration: 1 Sep 20145 Sep 2014
Conference number: 2014

Conference

Conference22nd European Signal Processing Conference
Abbreviated titleEUSIPCO
CountryPortugal
CityLisbon
Period1/09/145/09/14

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Keywords

  • automated human tracking
  • thermal video
  • temporal characteristic recognition
  • human detection
  • video signal processing
  • feature extraction
  • object detection
  • object tracking
  • spatial tracking methods
  • person specific time varying signatures

Cite this

Coutts, F. K., Marshall, S., & Murray, P. (2014). Human detection and tracking through temporal feature recognition. In 2014 22nd European Signal Processing Conference (EUSIPCO) (pp. 2180-2184). IEEE.
Coutts, Fraser K. ; Marshall, Stephen ; Murray, Paul. / Human detection and tracking through temporal feature recognition. 2014 22nd European Signal Processing Conference (EUSIPCO). IEEE, 2014. pp. 2180-2184
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abstract = "The ability to accurately track objects of interest – particularly humans – is of great importance in the fields of security and surveillance. In such scenarios, t he application of accurate, automated human tracking offers benefits over manual supervision. In this paper, recent efforts made to investigate the improvement of automated human detection and tracking techniques through the recognitionof person-specific time-varying signatures in thermal video are detailed. A robust human detection algorithm is developed to aid the initialisation stage of a state-of-the art existing tracking algorithm. In addition, coupled with the spatial tracking methods present in this algorithm, the inclusion of temporal signature recognition in the tracking process is shown to improve human tracking results.",
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note = "(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.",
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Coutts, FK, Marshall, S & Murray, P 2014, Human detection and tracking through temporal feature recognition. in 2014 22nd European Signal Processing Conference (EUSIPCO). IEEE, pp. 2180-2184, 22nd European Signal Processing Conference, Lisbon, Portugal, 1/09/14.

Human detection and tracking through temporal feature recognition. / Coutts, Fraser K.; Marshall, Stephen; Murray, Paul.

2014 22nd European Signal Processing Conference (EUSIPCO). IEEE, 2014. p. 2180-2184.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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Coutts FK, Marshall S, Murray P. Human detection and tracking through temporal feature recognition. In 2014 22nd European Signal Processing Conference (EUSIPCO). IEEE. 2014. p. 2180-2184