Performance assessment of an insect-inspired target tracking model in background clutter
(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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- author
- Bagheri, Zahra ; Wiederman, Steven D. LU ; Cazzolato, Benjamin S. ; Grainger, Steven and O'Carroll, David C. LU
- organization
- publishing date
- 2014-01-01
- 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
- 2022-02-01 08:57:15
@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}}, keywords = {{biological image processing; feature detection; image features; Target tracking}}, 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}}, }