Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/104019
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Type: Conference paper
Title: Performance assessment of an insect-inspired target tracking model in background clutter
Author: Bagheri
Wiederman
Cazzolato, B.
Grainger
O'Carroll
Citation: Proceedings of the 13th International Conference on Control, Automation, Robotics and Vision, ICARCV 2014, 2014 / pp.822-826
Publisher: IEEE
Issue Date: 2014
Series/Report no.: International Conference on Control Automation Robotics and Vision
ISBN: 9781479952007
ISSN: 2474-2953
Conference Name: 13th International Conference on Control, Automation, Robotics and Vision (ICARCV 2014) (10 Dec 2014 - 12 Dec 2014 : Singapore)
Statement of
Responsibility: 
Zahra Bagheri, Steven D. Wiederman, Benjamin S. Cazzolato, Steven Grainger, David C. O'Carroll
Abstract: Biological visual systems provide excellent examples of robust target detection and tracking mechanisms capable of performing in a wide range of environments. Consequently, they have been sources of inspiration for many artificial vision algorithms. However, testing the robustness of target detection and tracking algorithms is a challenging task due to the diversity of environments for applications of these algorithms. Correlation between image quality metrics and model performance is one way to deal with this problem. Previously we developed a target detection model inspired by physiology of insects and implemented it in a closed loop target tracking algorithm. In the current paper we vary the kinetics of a salience-enhancing element of our algorithm and test its effect on the robustness of our model against different natural images to find the relationship between model performance and background clutter.
Keywords: Target tracking; feature detection; biological image processing; image features
Rights: ©2014 IEEE
RMID: 0030027105
DOI: 10.1109/ICARCV.2014.7064410
Grant ID: http://purl.org/au-research/grants/arc/DP130104572
Appears in Collections:Mechanical Engineering conference papers

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