| Summary: | Driving at night is a difficult task. In an attempt to ease this task, most automotive
companies are developing systems that aim to increase the safety of the driver and
his/her passengers at night. Jaguar Cars Ltd have been involved in such project
for several years and have developed a Night Vision System (NVS) based upon the
Near Infrared (NIR) and Head-Up Display (HUD) technologies.
This thesis is concerned with the application of digital image enhancement algorithms
to further increase the driver's visual range at night. The purpose of this
research work is to provide the driver with a safe and non-disturbing, enhanced view
of the road scene ahead, which is presented on a head-up display.
In this automotive environment, specific requirements such as real-time processing,
robustness and reliability must be kept in mind to design algorithms that will not
compromise the safety of the driver, his/her passengers and other road users.
To fulfill these requirements, we have developed a novel intelligent image enhancement
scheme for night time driving that actively adapts to the road scene. This
scheme results in the enhancement of the contrast in a portion of the projected
HUD road scene as if extra headlamps were directed to the region of the image
that represents where the road is going. Human Factors studies have shown that
this region is where the driver is concentrating his attention when driving. The
position of the region of interest is defined by the computation of an approximation
of the vanishing point of the road, updated for every frame using a novel, reliable
and optimised road edge detection algorithm. The enhancement of the contrast
within the region of interest is obtained by applying several novel low-level algorithms
based upon the grey level segmentation of the image into regions and the use
of the global histogram equalisation and quantised bi-histogram equalisation algorithms.
These novel algorithms have all been implemented on the Matrox Genesis
board based upon the multitasking, multiprocessor and parallel DSP TMS320C80
chip from Texas Instruments. All algorithms described in this thesis are able to
sustain real-time processing at the NTSC frame rate of 30 frames per second.
This new concept for a night time driving aid is an attractive solution that meets
the numerous requirements driven by Human Factors research in an automotive
environment, in particular safety requirements.
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