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Performance assessment of an insect-inspired target tracking model in background clutter

Bagheri, Zahra ; Wiederman, Steven D. LU ; Cazzolato, Benjamin S. ; Grainger, Steven and O'Carroll, David C. LU (2014) 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 In 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014 p.822-826
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... (More)

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.

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Please use this url to cite or link to this publication:
author
; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
biological image processing, feature detection, image features, Target tracking
host publication
2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
series title
2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
article number
7064410
pages
822 - 826
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014
conference location
Singapore, Singapore
conference dates
2014-12-10 - 2014-12-12
external identifiers
  • scopus:84949924867
ISBN
9781479951994
DOI
10.1109/ICARCV.2014.7064410
language
English
LU publication?
yes
id
998fbaf0-03da-48ec-b7dc-778fed2e186b
date added to LUP
2020-10-01 15:39:06
date last changed
2021-12-02 08:41:22
@inproceedings{998fbaf0-03da-48ec-b7dc-778fed2e186b,
  abstract     = {<p>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.</p>},
  author       = {Bagheri, Zahra and Wiederman, Steven D. and Cazzolato, Benjamin S. and Grainger, Steven and O'Carroll, David C.},
  booktitle    = {2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014},
  isbn         = {9781479951994},
  language     = {eng},
  month        = {01},
  pages        = {822--826},
  publisher    = {IEEE - Institute of Electrical and Electronics Engineers Inc.},
  series       = {2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014},
  title        = {Performance assessment of an insect-inspired target tracking model in background clutter},
  url          = {http://dx.doi.org/10.1109/ICARCV.2014.7064410},
  doi          = {10.1109/ICARCV.2014.7064410},
  year         = {2014},
}