Robustness and Real-Time Performance of an Insect Inspired Target Tracking Algorithm Under Natural Conditions

Zahra Bagheri, Steven Wiederman, Benjamin Cazzolato, Steven Grainger, David O'Carroll

Research output: Chapter in Book/Conference paperConference paperpeer-review

1 Citation (Scopus)

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 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.
Original languageEnglish
Title of host publication2015 IEEE Symposium Series on Computational Intelligence
EditorsAndries Engelbrecht
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages97-102
DOIs
Publication statusPublished - 2015
Externally publishedYes
EventIEEE Symposium Series on Computational Intelligence, SSCI 2015 - Cape Town, South Africa
Duration: 8 Dec 201510 Dec 2015

Conference

ConferenceIEEE Symposium Series on Computational Intelligence, SSCI 2015
Country/TerritorySouth Africa
CityCape Town
Period8/12/1510/12/15

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