CLAVEAU, David and Wang, Chunyan (2007) Space-variant motion detection for active visual target tracking. Robotics and Autonomous Systems, 57 (1). pp. 11-22. ISSN 09218890 (In Press)
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Official URL: http://dx.doi.org/10.1016/j.robot.2008.04.001
Abstract
A biologically inspired approach to active visual target tracking is presented. The approach makes use of three strategies found in biological systems: space-variant sensing, a spatio-temporal frequency based model of motion detection and the alignment of sensory-motor maps. Space-variant imaging is used to create a 1-D array of elementary motion detectors (EMDs) that are tuned in such a way as to make it possible to detect motion over a wide range of velocities while still being able to detect motion precisely. The array is incorporated into an active visual tracking system. A method of analysis and design for such a tracking system is proposed. It makes use of a sensory-motor map which consists of a phase-plane plot of the continuous-time dynamics of the tracking system overlaid onto a map of the detection capabilities of the array of EMDs. This sensory-motor map is used to design a simple 1-D tracking system and several simulations show how the method can be used to control tracking performance using such metrics as overshoot and settling time. A complete 1-D active vision system is implemented and a set of simple target tracking experiments are performed to demonstrate the effectiveness of the approach.
Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering |
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Item Type: | Article |
Refereed: | Yes |
Authors: | CLAVEAU, David and Wang, Chunyan |
Journal or Publication: | Robotics and Autonomous Systems |
Date: | 19 July 2007 |
Digital Object Identifier (DOI): | 10.1016/j.robot.2008.04.001 |
Keywords: | Active vision; Sensory-motor coordination; Space-variant imaging; Visual motion tracking |
ID Code: | 36142 |
Deposited By: | Chunyan Wang |
Deposited On: | 07 Dec 2011 21:55 |
Last Modified: | 18 Jan 2018 17:36 |
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