dc.contributor.author |
Kanjee, R
|
|
dc.contributor.author |
Bachoo, AK
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|
dc.contributor.author |
Carroll, J
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|
dc.date.accessioned |
2016-08-23T08:03:05Z |
|
dc.date.available |
2016-08-23T08:03:05Z |
|
dc.date.issued |
2015-12 |
|
dc.identifier.citation |
Kanjee, R. Bachoo, A.K. and Carroll, J. 2015. A three-step vehicle detection framework for range estimation using a single camera. In: IEEE Symposium Series on Computational Intelligence 2015, Cape Town, 8-10 December 2015 |
en_US |
dc.identifier.uri |
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7376645&tag=1
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|
dc.identifier.uri |
http://hdl.handle.net/10204/8744
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|
dc.description |
IEEE Symposium Series on Computational Intelligence 2015, Cape Town, 8-10 December 2015. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website |
en_US |
dc.description.abstract |
This paper proposes and validates a real-time onroad vehicle detection system, which uses a single camera for the purpose of intelligent driver assistance. A three-step vehicle detection framework is presented to detect and track the target vehicle within an image. In the first step, probable vehicle locations are hypothesized using pattern recognition. The vehicle candidates are then verified in the hypothesis verification step. In this step, lane detection is used to filter vehicle candidates that are not within the lane region of interest. In the final step tracking and online learning are implemented to optimize the detection algorithm during misdetection and temporary occlusion. Good detection performance and accuracy was observed in highway driving environments with minimal shadows. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
IEEE Xplore |
en_US |
dc.relation.ispartofseries |
Workflow;15655 |
|
dc.subject |
Adaptive cruise control |
en_US |
dc.subject |
Adaptive image cropping |
en_US |
dc.subject |
Range estimation |
en_US |
dc.subject |
Monocular vision |
en_US |
dc.subject |
Pattern matching |
en_US |
dc.subject |
Vehicle detection |
en_US |
dc.title |
A three-step vehicle detection framework for range estimation using a single camera |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Kanjee, R., Bachoo, A., & Carroll, J. (2015). A three-step vehicle detection framework for range estimation using a single camera. IEEE Xplore. http://hdl.handle.net/10204/8744 |
en_ZA |
dc.identifier.chicagocitation |
Kanjee, R, AK Bachoo, and J Carroll. "A three-step vehicle detection framework for range estimation using a single camera." (2015): http://hdl.handle.net/10204/8744 |
en_ZA |
dc.identifier.vancouvercitation |
Kanjee R, Bachoo A, Carroll J, A three-step vehicle detection framework for range estimation using a single camera; IEEE Xplore; 2015. http://hdl.handle.net/10204/8744 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Kanjee, R
AU - Bachoo, AK
AU - Carroll, J
AB - This paper proposes and validates a real-time onroad vehicle detection system, which uses a single camera for the purpose of intelligent driver assistance. A three-step vehicle detection framework is presented to detect and track the target vehicle within an image. In the first step, probable vehicle locations are hypothesized using pattern recognition. The vehicle candidates are then verified in the hypothesis verification step. In this step, lane detection is used to filter vehicle candidates that are not within the lane region of interest. In the final step tracking and online learning are implemented to optimize the detection algorithm during misdetection and temporary occlusion. Good detection performance and accuracy was observed in highway driving environments with minimal shadows.
DA - 2015-12
DB - ResearchSpace
DP - CSIR
KW - Adaptive cruise control
KW - Adaptive image cropping
KW - Range estimation
KW - Monocular vision
KW - Pattern matching
KW - Vehicle detection
LK - https://researchspace.csir.co.za
PY - 2015
T1 - A three-step vehicle detection framework for range estimation using a single camera
TI - A three-step vehicle detection framework for range estimation using a single camera
UR - http://hdl.handle.net/10204/8744
ER -
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en_ZA |