Deep Neural Network-Based Cooperative Visual Tracking Through Multiple Micro Aerial Vehicles

Eric Price, Guilherme Lawless, Roman Ludwig, Igor Martinovic, Heinrich Bulthoff, Michael J. Black, Aamir Ahmad

Research output: Contribution to journalArticle

12 Citations (Scopus)


Multicamera tracking of humans and animals in outdoor environments is a relevant and challenging problem. Our approach to it involves a team of cooperating microaerial vehicles (MAVs) with on-board cameras only. Deep neural networks (DNNs) often fail at detecting small-scale objects or those that are far away from the camera, which are typical characteristics of a scenario with aerial robots. Thus, the core problem addressed in this letter is how to achieve on-board, online, continuous, and accurate vision-based detections using DNNs for visual person tracking through MAVs. Our solution leverages cooperation among multiple MAVs and active selection of most informative regions of image. We demonstrate the efficiency of our approach through simulations with up to 16 robots and real-robot experiments involving two aerial robots tracking a person, while maintaining an active perception-driven formation. ROS-based source code is provided for the benefit of the community.

Original languageEnglish
Pages (from-to)3193-3200
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number4
Publication statusPublished - 2018 Oct 1


  • aerial systems: Perception and autonomy
  • multirobot systems
  • Visual tracking

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Human-Computer Interaction
  • Biomedical Engineering
  • Mechanical Engineering
  • Control and Optimization
  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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