dc.contributor.author |
Dickens, JS
|
|
dc.contributor.author |
Green, JJ
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|
dc.contributor.author |
Van Wyk, MA
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|
dc.date.accessioned |
2011-10-10T09:29:43Z |
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dc.date.available |
2011-10-10T09:29:43Z |
|
dc.date.issued |
2011-07 |
|
dc.identifier.citation |
Dickens, JS, Green, JJ and Van Wyk, MA. 2011. Human detection for underground autonomous mine vehicles using thermal imaging. 26th International Conference on CAD/CAM, Robotics and Factories of the Future (CARs&FOF 2011), Kuala Lumpur, Malaysia, 26-28 July 2011 |
en_US |
dc.identifier.uri |
http://hdl.handle.net/10204/5213
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|
dc.description |
26th International Conference on CAD/CAM, Robotics and Factories of the Future (CARs&FOF 2011), Kuala Lumpur, Malaysia, 26-28 July 2011 |
en_US |
dc.description.abstract |
Underground mine automation has the potential to increase safety, productivity and allow the mining of lower-grade resources. In a mining environment with both autonomous robots and humans, it is essential that the robots are able to detect and avoid people. Current pedestrian detection systems and the reasons that they are inadequate for mining robots are discussed. A system for human detection in underground mines, using a fusion of three-dimensional (3D) information with thermal imaging, is proposed. The system extracts regions of interest and classifies them as human or background. The scene excluding the pedestrians is assumed to be static and is intended to be used to determine the ego motion of the vehicle. In addition to the thermal camera, a distance sensor will provide depth information and allow the calculation of the vehicle and pedestrian velocities. Various classification methods are compared and it is shown that a neural network provides the best results in terms of speed and accuracy. The results of tests on two 3D sensors indicate that further work is required to determine the effect of the harsh environment on the accuracy of the sensors. |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.ispartofseries |
Workflow request;7145 |
|
dc.subject |
Underground autonomous mine vehicles |
en_US |
dc.subject |
Thermal imaging |
en_US |
dc.subject |
Mine safety |
en_US |
dc.subject |
Underground mining |
en_US |
dc.subject |
Autonomous robots |
en_US |
dc.subject |
Obstacle detection |
en_US |
dc.subject |
Human tracking |
en_US |
dc.subject |
Robotics |
en_US |
dc.subject |
Mine vehicles |
en_US |
dc.title |
Human detection for underground autonomous mine vehicles using thermal imaging |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Dickens, J., Green, J., & Van Wyk, M. (2011). Human detection for underground autonomous mine vehicles using thermal imaging. http://hdl.handle.net/10204/5213 |
en_ZA |
dc.identifier.chicagocitation |
Dickens, JS, JJ Green, and MA Van Wyk. "Human detection for underground autonomous mine vehicles using thermal imaging." (2011): http://hdl.handle.net/10204/5213 |
en_ZA |
dc.identifier.vancouvercitation |
Dickens J, Green J, Van Wyk M, Human detection for underground autonomous mine vehicles using thermal imaging; 2011. http://hdl.handle.net/10204/5213 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Dickens, JS
AU - Green, JJ
AU - Van Wyk, MA
AB - Underground mine automation has the potential to increase safety, productivity and allow the mining of lower-grade resources. In a mining environment with both autonomous robots and humans, it is essential that the robots are able to detect and avoid people. Current pedestrian detection systems and the reasons that they are inadequate for mining robots are discussed. A system for human detection in underground mines, using a fusion of three-dimensional (3D) information with thermal imaging, is proposed. The system extracts regions of interest and classifies them as human or background. The scene excluding the pedestrians is assumed to be static and is intended to be used to determine the ego motion of the vehicle. In addition to the thermal camera, a distance sensor will provide depth information and allow the calculation of the vehicle and pedestrian velocities. Various classification methods are compared and it is shown that a neural network provides the best results in terms of speed and accuracy. The results of tests on two 3D sensors indicate that further work is required to determine the effect of the harsh environment on the accuracy of the sensors.
DA - 2011-07
DB - ResearchSpace
DP - CSIR
KW - Underground autonomous mine vehicles
KW - Thermal imaging
KW - Mine safety
KW - Underground mining
KW - Autonomous robots
KW - Obstacle detection
KW - Human tracking
KW - Robotics
KW - Mine vehicles
LK - https://researchspace.csir.co.za
PY - 2011
T1 - Human detection for underground autonomous mine vehicles using thermal imaging
TI - Human detection for underground autonomous mine vehicles using thermal imaging
UR - http://hdl.handle.net/10204/5213
ER -
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en_ZA |