Osgood, Thomas J. (2013) Semantic labelling of road scenes using supervised and unsupervised machine learning with lidar-stereo sensor fusion. PhD thesis, University of Warwick.

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Abstract

At the highest level the aim of this thesis is to review and develop reliable and efficient
algorithms for classifying road scenery primarily using vision based technology mounted
on vehicles. The purpose of this technology is to enhance vehicle safety systems in order
to prevent accidents which cause injuries to drivers and pedestrians.
This thesis uses LIDAR–stereo sensor fusion to analyse the scene in the path of the vehicle
and apply semantic labels to the different content types within the images. It details every
step of the process from raw sensor data to automatically labelled images.
At each stage of the process currently used methods are investigated and evaluated. In
cases where existingmethods do not produce satisfactory results improvedmethods have
been suggested. In particular, this thesis presents a novel, automated,method for aligning
LIDAR data to the stereo camera frame without the need for specialised alignment grids.
For image segmentation a hybrid approach is presented, combining the strengths of both
edge detection and mean-shift segmentation. For texture analysis the presented method
uses GLCM metrics which allows texture information to be captured and summarised
using only four feature descriptors compared to the 100’s produced by SURF descriptors.
In addition to texture descriptors, the ìD information provided by the stereo system is
also exploited. The segmented point cloud is used to determine orientation and curvature
using polynomial surface fitting, a technique not yet applied to this application.
Regarding classification methods a comprehensive study was carried out comparing the
performance of the SVM and neural network algorithms for this particular application.
The outcome shows that for this particular set of learning features the SVM classifiers
offer slightly better performance in the context of image and depth based classification
which was not made clear in existing literature.
Finally a novel method of making unsupervised classifications is presented. Segments are
automatically grouped into sub-classes which can then be mapped to more expressive
super-classes as needed. Although the method in its current state does not yet match the
performance of supervised methods it does produce usable classification results without
the need for any training data. In addition, the method can be used to automatically
sub-class classes with significant inter-class variation into more specialised groups prior
to being used as training targets in a supervised method.

Item Type:	Thesis or Dissertation (PhD)
Subjects:	H Social Sciences > HE Transportation and Communications Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering
Library of Congress Subject Headings (LCSH):	Semantic computing, Machine learning, Optical radar, Image processing -- Digital techniques, Pattern recognition systems, Neural networks (Computer science), Image segmentation, Traffic safety -- Technological innovations
Official Date:	August 2013
Dates:	Date Event August 2013 Submitted
Institution:	University of Warwick
Theses Department:	Warwick Manufacturing Group
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Rushforth, Emma; Young, Ken; Huang, Yingping
Extent:	xxi, 251 leaves : illustrations.
Language:	eng

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