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Robustness and real-time performance of an insect inspired target tracking algorithm under natural conditions

Bagheri, Zahra ; Wiederman, Steven D. LU ; Cazzolato, Ben ; Grainger, Steven and O'Carroll, David C. LU (2015) IEEE Symposium Series on Computational Intelligence, SSCI 2015 In Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015 p.97-102
Abstract

Many computer vision tasks require the implementation of robust and efficient target tracking algorithms. Furthermore, in robotic applications these algorithms must perform whilst on a moving platform (ego motion). Despite the increase in computational processing power, many engineering algorithms are still challenged by real-Time applications. In contrast, lightweight and low-power flying insects, such as dragonflies, can readily chase prey and mates within cluttered natural environments, deftly selecting their target amidst distractors (swarms). In our laboratory, we record from 'target-detecting' neurons in the dragonfly brain that underlie this pursuit behavior. We recently developed a closed-loop target detection and tracking... (More)

Many computer vision tasks require the implementation of robust and efficient target tracking algorithms. Furthermore, in robotic applications these algorithms must perform whilst on a moving platform (ego motion). Despite the increase in computational processing power, many engineering algorithms are still challenged by real-Time applications. In contrast, lightweight and low-power flying insects, such as dragonflies, can readily chase prey and mates within cluttered natural environments, deftly selecting their target amidst distractors (swarms). In our laboratory, we record from 'target-detecting' neurons in the dragonfly brain that underlie this pursuit behavior. We recently developed a closed-loop target detection and tracking algorithm based on key properties of these neurons. Here we test our insect-inspired tracking model in open-loop against a set of naturalistic sequences and compare its efficacy and efficiency with other state-of-The-Art engineering models. In terms of tracking robustness, our model performs similarly to many of these trackers, yet is at least 3 times more efficient in terms of processing speed.

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author
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organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015
series title
Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015
article number
7376597
pages
97 - 102
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
IEEE Symposium Series on Computational Intelligence, SSCI 2015
conference location
Cape Town, South Africa
conference dates
2015-12-08 - 2015-12-10
external identifiers
  • scopus:84964933151
ISBN
9781479975600
DOI
10.1109/SSCI.2015.24
language
English
LU publication?
yes
id
72a705e4-69f5-4ad3-87ea-ec257ef968a9
date added to LUP
2020-10-01 15:42:51
date last changed
2022-02-01 08:57:15
@inproceedings{72a705e4-69f5-4ad3-87ea-ec257ef968a9,
  abstract     = {{<p>Many computer vision tasks require the implementation of robust and efficient target tracking algorithms. Furthermore, in robotic applications these algorithms must perform whilst on a moving platform (ego motion). Despite the increase in computational processing power, many engineering algorithms are still challenged by real-Time applications. In contrast, lightweight and low-power flying insects, such as dragonflies, can readily chase prey and mates within cluttered natural environments, deftly selecting their target amidst distractors (swarms). In our laboratory, we record from 'target-detecting' neurons in the dragonfly brain that underlie this pursuit behavior. We recently developed a closed-loop target detection and tracking algorithm based on key properties of these neurons. Here we test our insect-inspired tracking model in open-loop against a set of naturalistic sequences and compare its efficacy and efficiency with other state-of-The-Art engineering models. In terms of tracking robustness, our model performs similarly to many of these trackers, yet is at least 3 times more efficient in terms of processing speed.</p>}},
  author       = {{Bagheri, Zahra and Wiederman, Steven D. and Cazzolato, Ben and Grainger, Steven and O'Carroll, David C.}},
  booktitle    = {{Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015}},
  isbn         = {{9781479975600}},
  language     = {{eng}},
  month        = {{01}},
  pages        = {{97--102}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015}},
  title        = {{Robustness and real-time performance of an insect inspired target tracking algorithm under natural conditions}},
  url          = {{http://dx.doi.org/10.1109/SSCI.2015.24}},
  doi          = {{10.1109/SSCI.2015.24}},
  year         = {{2015}},
}