Risse, B., Mangan, M. orcid.org/0000-0002-0293-8874, Webb, B. et al. (1 more author) (2018) Visual tracking of small animals in cluttered natural environments using a freely moving camera. In: 2017 IEEE International Conference on Computer Vision Workshops. IEEE International Conference on Computer Vision Workshops (ICCVW), 22-29 Oct 2017, Venice, Italy. IEEE , pp. 2840-2849. ISBN 9781538610350
Abstract
Image-based tracking of animals in their natural habitats can provide rich behavioural data, but is very challenging due to complex and dynamic background and target appearances. We present an effective method to recover the positions of terrestrial animals in cluttered environments from video sequences filmed using a freely moving monocular camera. The method uses residual motion cues to detect the targets and is thus robust to different lighting conditions and requires no a-priori appearance model of the animal or environment. The detection is globally optimised based on an inference problem formulation using factor graphs. This handles ambiguities such as occlusions and intersections and provides automatic initialisation. Furthermore, this formulation allows a seamless integration of occasional user input for the most difficult situations, so that the effect of a few manual position estimates are smoothly distributed over long sequences. Testing our system against a benchmark dataset featuring small targets in natural scenes, we obtain 96% accuracy for fully automated tracking. We also demonstrate reliable tracking in a new data set that includes different targets (insects, vertebrates or artificial objects) in a variety of environments (desert, jungle, meadows, urban) using different imaging devices (day / night vision cameras, smart phones) and modalities (stationary, hand-held, drone operated).
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 18 Dec 2019 13:54 |
Last Modified: | 19 Dec 2019 11:01 |
Published Version: | http://openaccess.thecvf.com/content_ICCV_2017_wor... |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/ICCVW.2017.335 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:154758 |