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

Price, E; Lawless, G; Ludwig, R; Martinović, I; Bülthoff, HH; Black, MJ; Ahmad, A

doi:10.1109/LRA.2018.2850224

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

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Bülthoff, HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Price, E., Lawless, G., Ludwig, R., Martinović, I., Bülthoff, H., Black, M., et al. (2018). Deep Neural Network-Based Cooperative Visual Tracking Through Multiple Micro Aerial Vehicles. IEEE Robotics and Automation Letters, 3(4): TuBTS2.2, 3193-3200. doi:10.1109/LRA.2018.2850224.

Cite as: https://hdl.handle.net/21.11116/0000-0002-478F-E

Abstract

Multi-camera tracking of humans and animals in outdoor environments is a relevant and challenging problem. Our approach to it involves a team of cooperating micro aerial 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 paper 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.