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|Title:||Performance assessment of an insect-inspired target tracking model in background clutter|
|Citation:||Proceedings of the 13th International Conference on Control, Automation, Robotics and Vision, ICARCV 2014, 2014 / pp.822-826|
|Series/Report no.:||International Conference on Control Automation Robotics and Vision|
|Conference Name:||13th International Conference on Control, Automation, Robotics and Vision (ICARCV 2014) (10 Dec 2014 - 12 Dec 2014 : Singapore)|
|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|
|Appears in Collections:||Mechanical Engineering conference papers|
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