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μ-UAV Based Dynamic Target Tracking for Surveillance and Exploration

  • Harish Bhaskar
  • Jorge Dias
  • Lakmal Seneviratne
  • Mohammed Al-Mualla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8887)

Abstract

This paper presents an autonomous computer vision system for tracking multiple dynamic ground targets, from images acquired by a camera onboard of a μ-UAV. The method proposed is a self adaptive technique that seamlessly integrates ego-motion compensation with target detection and tracking to provide robust localisation of ground targets. Ego-motion compensation is achieved through establishing homographies using target independent invariant feature descriptors. Targets are then detected using a novel background learning strategy where the optical flow field is fused together with a dynamic background model for accurate foreground extraction. In addition, the paper also reports the use of a Monte Carlo joint probabilistic data association filter for tracking multiple unknown targets. The field tests demonstrate the capabilities of the vision system based on experimental results on images captured by a camera on-board of quadrators (μ-UAV).

Keywords

Gaussian Mixture Model Target Detection Target Tracking Ground Target Gaussian Probability Density Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Harish Bhaskar
    • 1
  • Jorge Dias
    • 1
  • Lakmal Seneviratne
    • 1
  • Mohammed Al-Mualla
    • 1
  1. 1.Technology and ResearchKhalifa University of ScienceAbu DhabiU.A.E.

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