A real-world vision system: mechanism, control, and vision processing

Authors:
Andrew Dankers;Alexander Zelinsky
Affiliations:
Robotic Systems Laboratory, Research School of Information Sciences and Engineering, The Australian National University, Canberra, ACT, Australia;Robotic Systems Laboratory, Research School of Information Sciences and Engineering, The Australian National University, Canberra, ACT, Australia
Venue:
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Year:
2003

Citing 5
Cited 0

Animate vision

Artificial Intelligence
A head-eye system—analysis and design

CVGIP: Image Understanding - Special issue on purposive, qualitative, active vision
CONDENSATION—Conditional Density Propagation forVisual Tracking

International Journal of Computer Vision
Active Stereo Vision System with Foveated Wide Angle Lenses

ACCV '95 Invited Session Papers from the Second Asian Conference on Computer Vision: Recent Developments in Computer Vision
An Adaptive Fusion Architecture for Target Tracking

FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper reports on the development of a multi-purpose active visual sensor system for real-world application. The Cable-Drive Active-Vision Robot (CeDAR) has been designed for use on a diverse range of platforms, to perform a diverse range of tasks. The novel, biologically inspired design has evolved from a systems based approach. The mechanism is compact and light-weight, and is capable of motions that exceed human visual performance and earlier mechanical designs. The control system complements the mechanical design to implement the basic visual behaviours of fixation, smooth pursuit and saccade, with stability during high speed motions, high precision and repeatability. Real-time vision processing algorithms have been developed that process stereo colour images at 30Hz, resulting in a suite of basic visual competencies. We have developed a scheme to fuse the results of the visual algorithms into robust task-oriented behaviours by adopting a statistical frame-work. CeDAR has been successfully used for experiments in autonomous vehicle guidance, object tracking, and visual sensing for mobile robot experiments.