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Space-variant motion detection for active visual target tracking

Title:

Space-variant motion detection for active visual target tracking

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
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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