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UAV target tracking by detection via deep neural networks

  • Mohamed A. Kassab
  • , Ali Maher
  • , Fathy Elkazzaz
  • , Zhang Baochang*
  • *Corresponding author for this work
  • Beihang University
  • Military Technical College
  • Benha University
  • Shenzhen Academy of Aerospace Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper introduces an Image Based Visual Servoing (IBVS) system to track a ground target from a flying UAV. We implement a tracking system via two deep neural networks; approaching network and chasing network. In the approaching network, the UAV-target relative distance is estimated through the tracked target state to drive the UAV to a desired distance. In the chasing network, the tracked target orientation is used in resolving the ambiguity between the UAV's yawing and lateral movements and aligning the UAV with the tracked target heading to ease the tracking task. The detection accuracy for both networks has been validated and a series of real flight data, which has been conducted on ground targets and persons showed the proposed system efficiency in target tracking and following.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Multimedia and Expo, ICME 2019
PublisherIEEE Computer Society
Pages139-144
Number of pages6
ISBN (Electronic)9781538695524
DOIs
StatePublished - Jul 2019
Event2019 IEEE International Conference on Multimedia and Expo, ICME 2019 - Shanghai, China
Duration: 8 Jul 201912 Jul 2019

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
Volume2019-July
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Conference

Conference2019 IEEE International Conference on Multimedia and Expo, ICME 2019
Country/TerritoryChina
CityShanghai
Period8/07/1912/07/19

Keywords

  • Approaching and chasing
  • IBVS
  • Two deep networks
  • UAV-target following

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