Deep Neural Network-Based Cooperative Visual Tracking Through Multiple Micro Aerial Vehicles
Open accessAbstract
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.
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Collective adaptive capabilities are observed in some species of group-living animals, such as swarms of midges, schools of fish, and herds of sheep.
“Such collective capabilities are observed in some species of group-living animals (swarms of midges, schools of fish, and herds of sheep).”
From Swarm Robotics: Past, Present, and Future [Point of View] (2021) · cites this paper as [90]
In these species, interactions between individuals give rise to group properties similar to those of a physical system close to a phase transition between two macroscopic states, resulting in extreme sensitivity to changes in the behavior of a small number of individuals.
“In these species, the interactions between individuals give rise to group properties similar to those of a physical system close to a phase transition between two macroscopic states (see the glossary in Table 1), resulting in an extreme sensitivity to changes in the behavior of a small number of individuals [90], [91].”
From Swarm Robotics: Past, Present, and Future [Point of View] (2021) · cites this paper as [90]
Cites
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works
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DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning
Graving, J. M., Chae, D., Naik, H., Li, L., Koger, B., Costelloe, B. R., & Couzin, I. D. (2019) · eLife · doi:10.7554/elife.47994 · Cited by 547 · see citing works
Swarm Robotics: Past, Present, and Future [Point of View]
Dorigo, M., Theraulaz, G., & Trianni, V. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3072740 · Cited by 401 · see citing works
- Authors
- Price, E., Lawless, G., Ludwig, R., Martinovic, I., Bulthoff, H. H., Black, M. J., & Ahmad, A.
- Affiliations
- Max Planck Institute for Intelligent Systems (DE)
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2018-10
- Volume
- 3
- Issue
- 4
- Pages
- 3193-3200
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W2785754701
- Cited by
- 64
- References
- 1
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 6 · 2023: 6 · 2022: 7 · 2021: 6 · 2020: 12 · 2019: 13 · 2018: 3
- FWCI
- 4.25
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- IEEE Robotics and Automation Letters (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Max Planck Grassroots project MAVOCAP
- Perceiving Systems department, MPI for Intelligent Systems
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- micro air vehicles, human tracking, deep neural network, active perception, formation control
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