Combining front vehicle detection with 3D pose estimation for a better driver assistance

Peng, Y; Xu, M; Ni, Z; Jin, JS; Luo, S

Combining front vehicle detection with 3D pose estimation for a better driver assistance

Peng, Y Xu, M

Ni, Z Jin, JS Luo, S

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Publication Type:: Journal Article
Citation:: International Journal of Advanced Robotic Systems, 2012, 9
Issue Date:: 2012-09-28

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Field	Value	Language
dc.contributor.author	Peng, Y	en_US
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849	en_US
dc.contributor.author	Ni, Z	en_US
dc.contributor.author	Jin, JS	en_US
dc.contributor.author	Luo, S	en_US
dc.date.issued	2012-09-28	en_US
dc.identifier.citation	International Journal of Advanced Robotic Systems, 2012, 9	en_US
dc.identifier.issn	1729-8806	en_US
dc.identifier.uri	http://hdl.handle.net/10453/23040
dc.description.abstract	Driver assistant systems enhance traffic safety and efficiency. The accurate 3D pose of a front vehicle can help a driver to make the right decision on the road. We propose a novel real-time system to estimate the 3D pose of the front vehicle. This system consists of two parallel threads: vehicle rear tracking and mapping. The vehicle rear is first identified in the video captured by an onboard camera, after license plate localization and foreground extraction. The 3D pose estimation technique is then employed with respect to the extracted vehicle rear. Most current 3D pose estimation techniques need prior models or a stereo initialization with user cooperation. It is extremely difficult to obtain prior models due to the varying appearance of vehicles' rears. Moreover, it is unsafe to ask for drivers' cooperation when a vehicle is running. In our system, two initial keyframes for stereo algorithms are automatically extracted by vehicle rear detection and tracking. Map points are defined as a collection of point features extracted from the vehicle's rear with their 3D information. These map points are inferences that relate the 2D features detected in following vehicles' rears with the 3D world. The relative 3D pose of the onboard camera to the front vehicle rear is then estimated through matching the map points with point features detected on the front vehicle rear. We demonstrate the capabilities of our system by testing on real-time and synthesized videos. In order to make the experimental analysis visible, we demonstrated an estimated 3D pose through augmented reality, which needs accurate and real-time 3D pose estimation. © 2011 Peng et al.	en_US
dc.relation.ispartof	International Journal of Advanced Robotic Systems	en_US
dc.relation.isbasedon	10.5772/50530	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Combining front vehicle detection with 3D pose estimation for a better driver assistance	en_US
dc.type	Journal Article
utslib.citation.volume	9	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	17 Psychology and Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	9	en_US

Abstract:

Driver assistant systems enhance traffic safety and efficiency. The accurate 3D pose of a front vehicle can help a driver to make the right decision on the road. We propose a novel real-time system to estimate the 3D pose of the front vehicle. This system consists of two parallel threads: vehicle rear tracking and mapping. The vehicle rear is first identified in the video captured by an onboard camera, after license plate localization and foreground extraction. The 3D pose estimation technique is then employed with respect to the extracted vehicle rear. Most current 3D pose estimation techniques need prior models or a stereo initialization with user cooperation. It is extremely difficult to obtain prior models due to the varying appearance of vehicles' rears. Moreover, it is unsafe to ask for drivers' cooperation when a vehicle is running. In our system, two initial keyframes for stereo algorithms are automatically extracted by vehicle rear detection and tracking. Map points are defined as a collection of point features extracted from the vehicle's rear with their 3D information. These map points are inferences that relate the 2D features detected in following vehicles' rears with the 3D world. The relative 3D pose of the onboard camera to the front vehicle rear is then estimated through matching the map points with point features detected on the front vehicle rear. We demonstrate the capabilities of our system by testing on real-time and synthesized videos. In order to make the experimental analysis visible, we demonstrated an estimated 3D pose through augmented reality, which needs accurate and real-time 3D pose estimation. © 2011 Peng et al.

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http://hdl.handle.net/10453/23040