Night vision technology in automobile

SEMINAR REPORT ON
“NIGHT VISION SYSTEM IN AUTOMOBILE”
Submitted in partial fulfillment of the requirement for awarding the degree of
Bachelor of engineering in Industrial engineering and Management Of
Visvesvaraya Technological University during the year 2016 conducted at
Bangalore Institute of Technology, Bangalore.
Submitted By
CHETANA M.S
(1BI14IM401)
DEPARTMENT OF INDUSTRIAL ENGINEERING & MANAGEMENT
BANGALORE INSTITUTE OF TECHNOLOGY
BANGALORE-560 004
2016-2017
SUMMARY
Department of Industrial Engineering , BIT, Bangalore 560004 Page 1

Night vision is one of the major advancement in vehicle safety systems. It enables the
better visibility of the field in which vehicle is driven during the night time. Studies
report that, there is only quarter of the all travel by car drivers is undertaken a night
drive, but still, 40% of the road accidents happens during night time. This makes
night vision system demanding for drivers assist during poor light or during night
time.
The major reason for night accidents is poor visibility of the field of driving due to the
limitation in head light range and the dazzling of high beam headlight from the
vehicle that approaches from the opposite direction. Though night vision system
available in the market minimizes the occurrence and consequences of automobile
accidents, it is not 100% efficient for the ease and pleasure of driving for the old aged
drivers. Since its display is limited to a small screen which provides only a
monochrome output, the driver doesn't tent to depend on night vision all the time.

NIGHT VISION SYSTEM IN
AUTOMOBILE 2016
With this paper work we are trying to highlight the advancement of night
vision which can convert the present monochromatic display to a
colourised one and help driver with a better assist
TABLE OF CONTENTS
INTRODUCTION.........................................................................................................................4
1.NIGHT VISION SYSYTEM
2.NIGHT VISION SYSTEM IN AUTOMOBILE..................................................................5
2.1 INFRARED PROJECTORS................................................................................................6
2.2 NIGHT VISION CAMERA.................................................................................................7
2.3 IMAGE INTENSIFIER....................................................................................................... 8
2.4 INFRARED SENSORS......................................................................................................10
2.5 NIGHT VISION PROCESSING UNIT3. WORKING OF AUTOMOTIVE NIGHT VISION
SYSTEM.......................................................................................................................................11
4. ADVANCEMENT IN NIGHT VISION SYSTEM..........................................................13
4.1 PEDESTRIAN DETECTION SYSTEM.........................................................................14
4.1.1 CHARACTERSTICS OF IR DOMAIN........................................................................15
4.1.2 WORKING OF PEDESTRIAN DETECTION SYSTEM........................................15
4.1.3 PEDESTRIAN DETECTION ALGORITHM............................................................17
4.1.3.1 CONTOUR BASED CANDIDATE AREA EXTRACTION.................................19
4.1.3.2 CANDIDATE AREA CLASSIFICATION............................................................... 22
4.1.3.3 CANDIDATE AREA TRACKING.............................................................................23
4.2 INTELLIGENT VISION FOR AUTOMOBILES AT NIGHT....................................24
4.2.1 WORKING OF IVAN.......................................................................................................27
4.3 TRUE COLOR NIGHT VISION........................................................................................31
4.3.1 DESCRIPTION OF CAMERAS.....................................................................................34
4.3.1.1 LIQUID CRYSTAL FILTER INTENSIFIED CAMERA......................................34
5. APPLICATIONS......................................................................................................................38
6.CASE STUDY............................................................................................................................39
7.CONCLUSION...........................................................................................................................43

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INTRODUCTION
1. NIGHT VISION SYSTEM.
Night vision system is the technology developed for the clear visibility of the field of an
object during the night time or under poor light. Night vision technology was first
developed for the military activities. Later on the technology was adopted in
commercial purpose such as for automobiles and aircrafts.
Night vision system works based on the infrared rays. Infrared rays are a property of
light which falls under the wavelength of 700 nanometre to 1 millimetre. The infrared
rays are divided into three categories; Near Infrared, Mid Infrared and Far Infrared or
Thermal Infrared. The key difference between Thermal-IR and the other two is that
thermal-IR is emitted by an object instead of reflected off it. Infrared light is emitted by
an object because of what is happening at the atomic level. Ever particle reflects light.
While considering a particle in its atomic level, when it is subjected to light, some
particle absorb light in the form of heat and some reflects it back at a varying
wavelength. These reflected photons may not come under the visible range of the
humans, as human eyes have its limitations when compared to animal, but sensors can
do. Also, anything that is alive uses energy, and so do many inanimate items such as
engines and rockets. Energy consumption generates heat. In turn, heat causes the
atoms in an object to fire off photons in the thermal-infrared spectrum. The hotter the
object, the shorter the wavelength of the infrared photon it releases. Thermal imaging
takes advantage of this infrared emission. An object that is very hot will even begin to
emit photons in the visible spectrum, glowing red and then moving up through orange,
yellow, blue and eventually white. These reflected and emitted radiations which come
under the infrared regions are detected by IR sensors and cameras to generate a
monochromatic image that gives a better visibility of the field of view during low light.
Presently, there are two types of Night Vision technologies on the market, Far Infrared
(FIR) and Near Infrared (NIR). As stated above, FIR detects the radiation which all
objects emit, while NIR detects the reflected illumination in a frequency just outside
the visible range of a human being. This paper will analyse the requirements of a Night

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Vision system, how NIR and FIR today perform under the defined condition and
proceed to discuss directions for future development.
2. NIGHT VISION SYSTEM IN AUTOMOBILE
All cars today have an acceptable ‘night vision’ system. That is, the high beam
headlights of the vehicle. Even though they could be improved, their performances are
at least acceptable. However, in many areas, high beams are of very limited use due to
oncoming traffic. The insufficient night-time visibility originates in the fact that the
high beam headlights are rarely possible to use. A Night Vision system must therefore
be a system that increases visibility in situations where only low beam headlights can
be used. Studies report that, there is only quarter of the all travel by car drivers is
undertaken a night drive, but still, 40% of the road accidents happens during night
time. This makes night vision system demanding for drivers assist during poor light or
during night time.
The major reason for night accidents is poor visibility of the field of driving due to the
limitation in low beam head light range and the dazzling of high beam headlight from
the vehicle that approaches from the opposite direction. Though night vision system
available in the market minimizes the occurrence and consequences of automobile
accidents, it is not 100% efficient for the ease and pleasure of driving for the old aged
drivers. This condition thus define the importance and need to implicated technologies
for the safety of pedestrians during the night time, and better aid for the driver to
understand his field of view at a comfortable level.
The short detection distances for especially dark objects under low beam conditions
versus the corresponding situation under high beam condition illustrate the detection
distance deficiency that a Night Vision system should overcome. Safe driving speed
should allow the driver to detect, react and stop in time before any obstacles on the
road. However, most motorists actually drive faster than the visibility range allow with
low beam headlights.
The present night vision system used is automobile is a combination of NIR with image
intensifier and FIR with thermal imaging. The night vision system uses an infrared
projector, a camera, a processing unit and a display.

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2.1 INFRARED PROJECTORS
Night vision system works on the principle of infrared
rays. Infrared rays are invisible light rays which a
human eye cannot capture. These infrared rays are
generated using infrared LEDs and infrared laser
beams. The LEDs are used for NIR system while the
infrared laser is used for FIR to get a long range view. Now, in modern cars with night
vision system, the infrared projectors are attached along with the head lights rather
than being placed separately.
Image 1: Infrared Projector
2.2 NIGHT VISION CAMERA
The present night vision cameras used in automobile applications are very compact
and easy to accommodate. Some car manufacturers make a built in night vision system
in their cars. But some offer it as an added choice for the customer.

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Image 2: Night vision camera.
Like normal DSLR cameras, the night vision camera consist of a lens part often known
as image intensifier and a photon detecting sensor which can sense IR radiation.
2.3 IMAGE INTENSIFIER
Image-enhancement technology is what most people think of when you talk about
night vision. In fact, image-enhancement systems are normally called night-vision
devices (NVDs). NVDs rely on a special tube, called an image-intensifier tube, to collect
and amplify infrared and visible light. In night vision system, a conventional lens,
called the objective lens, captures ambient light and some near-infrared light. The
gathered light is sent to the image-intensifier tube. The image-intensifier tube has a
photocathode, which is used to convert the photons of light energy into electrons. As
the electrons pass through the tube, similar electrons are released from atoms in the
tube, multiplying the original number of electrons by a factor of thousands through the
use of a micro channel plate (MCP) in the tube. A MCP is a tiny glass disc that has
millions of microscopic holes (micro channels) in it, made using fibre-optic
technology. The MCP is contained in a vacuum and has metal electrodes on either side
of the disc. Each channel is about 45 times longer than it is wide, and it works as an
electron multiplier. When the electrons from the photo cathode hit the first electrode
of the MCP, they are accelerated into the glass micro channels by the 5,000-V bursts
being sent between the electrode pair. As electrons pass through the micro channels,
they cause thousands of other electrons to be released in each channel using a process
called cascaded secondary emission. Basically, the original electrons collide with the
side of the channel, exciting atoms and causing other electrons to be released. These

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new electrons also collide with other atoms, creating a chain reaction that results in
thousands of electrons leaving the channel where only a few entered. An interesting
fact is that the micro channels in the MCP are created at a slight angle (about a 5-
degree to 8-degree bias) to encourage electron collisions and reduce both ion and
direct-light feedback from the phosphors on the output side. At the end of the image-
intensifier tube, the electrons hit a screen coated with phosphors. These electrons
maintain their position in relation to the channel they passed through, which provides
a perfect image since the electrons stay in the same alignment as the original photons.
The energy of the electrons causes the phosphors to reach an excited state and release
photons. These phosphors create the green image on the screen that has come to
characterize night vision. The green phosphor image is viewed through another lens,
called the ocular lens, which allows you to magnify and focus the image. The NVD may
be connected to an electronic display, such as a monitor, or the image may be viewed
directly through the ocular lens.

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Image 3: Photon multiplying phenomenon of the photon received in image intensifier
Image 4: The figure demonstrates the path of one electron multiplying through one of
the millions of channels in the MCP. With each bounce the electron multiplies and
accelerates.
2.4 INFRARED SENSORS
Infrared sensor is the device that detects the infrared radiation in the light band. These
sensors are made out of silicon material which gets excited when the infrared rays
falls over it. These sensors can sense the infrared rays ranging from the wavelength

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700 nanometre to 1 millimetre. They can sense both NIR and FIR and provide varying
electric signal for the detected photons of varying wavelength. These electric signals
are amplified and processed to generate graphic signals and displayed on an output
device.
Image 5: Infrared sensor.
2.5 NIGHT VISION PROCESSING UNIT
The night vision processing unit is the main part of a night vision system, it process the
signal obtained from the infrared sensors to the digital visual signal. The night vision
processing unit determines the conditions of the field and do the required
amplification of the signal to give a better output. Now there are more complicated
control unit which can perform a verity of functions such as reducing the noise level
in the output, spotting height intensity lights in the field of ride and screening them to

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not to cause bright spot in the display screen etc.
The advanced night vision processing unit work along with the other safety and driver
assist systems available in automobiles to provide an intelligent night vision system.
Image 6: Night vision processing unit
3. WORKING OF AUTOMOTIVE NIGHT VISION SYSTEM
In car night vision system, during low light, the infrared projectors project the IR rays
on the field of driving. The infrared LEDs emits photons towards the field, these rays
are reflected by the surrounding. These reflected rays are captured by the night vision
camera in the car and is detected by IR sensors. The signal is then converted to image
signals and which is displayed through the display unit.

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Image 7:
Night
vision
system in
a car.
Image 8: Circuit diagram of Night vision system.
4. ADVANCEMENT IN NIGHT VISION SYSTEM
Over the years the automotive night vision technology has evolved a lot, now the night
vision is an intelligent vision system which can act upon base on the situation and
inform the driver like a co-driver. The influence of power electronics and navigation
has made the night vision system more comfortable for night drive.
4.1 PEDESTRIAN DETECTION SYSTEM

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The capability of observing the world through visual information is a strong
requirement for future driver assistance systems since their dues are getting more
complex. Especially, driver assistance systems dedicated to reduce the number of
fatalities and severities of traffic accidents impose several requirements on the
sensorial system. One of the major and challenging tasks is the detection and
classification of pedestrians.
Naturally, the use of visual cameras is a promising approach to cope with the
demands of pedestrian detection. Several different image processing methods and
systems have been developed in the last few years, including shape-based methods,
texture and template based methods, stereo, as well as motion clues. But none of this
is efficient in detecting pedestrian during the night time as the works based on the
light.
In order to facilitate the recognition process and to enable the detection of
pedestrian in dark environments passive infrared (IR) cameras have come into focus.
Some first pedestrian detection systems for IR images and videos have been
developed demonstrating the potential and benefits that IR cameras can provide.
Pedestrian detection using IR rays uses FIR or thermal infrared detection to identify
the pedestrian or animal in the field. Every living thing and working engines
generates energy in the form of heat radiations. During the night time, the non-living
thing in the surrounding environment stays cool. This provides suitable condition for
the thermal image sensors to detect the source that emit heat radiation.
4.1.1 CHARACTERIZATION OF IR DOMAIN
Images in the IR domain convey a type of information very different from images in
the visible spectrum. In the IR domain the image of an object relates to its temperature
and the amount of heat it emits but is not affected by illumination changes.
Generally, the temperature of people is higher than the environmental temperature

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and their heat radiation is sufficiently high compared to the background. Therefore, in
IR images pedestrians are bright and sufficiently contrasted with respect to the back-
ground, thus making IR imagery suited to their localization. Other objects which
actively radiate heat (cars, trucks etc.) have a similar behaviour; however people can
be recognized thanks to their shape and aspect ratio.
One major point in favour of IR cameras is the independency to lighting changes: IR
cameras can be used in day-time or night-time with no or little difference extending
vision beyond the usual limitations of day-light cameras. Moreover, the absence of
colours or strong textures eases the processing towards interpretation. Furthermore,
the problem of shadows is greatly reduced.
4.1.2. WORKING OF PEDESTRIAN DETECTION SYSTEM
The main deal with the pedestrian detection system is to identify the presence of
pedestrians or animal nearby the field of driving and to predict and inform and warn
the drive based on the behaviour of the identified object. For this, a serious of
processing and calculations are been done by the night vision control unit to
determine the position, behaviour and size of object. All these things are been done
with the help of real time image processing.
Since the vehicle is in continuous movement, tracking the position of the detected
object is a bit complicated task. For this the image processing unit uses multiple
frames of images at an in travel of time, and relates with the vehicle seed to determine
the relative position of the object.
The ratios of the polar coordinates of the detected images at consecutive intervals
determine the size and type of the identified object.
When an object is detected, a bounding box appears on the screen to indicate the
position of it in the output screen.

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Image 9: Automotive
Pedestrian detection system
Image 10: Flow chart of pedestrian detection system

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4.1.3. PEDESTRIAN DETECTION ALGORITHM
The process flowchart of the newly proposed pedestrian detection algorithm is shown
in Image 11. In many cases of night time pedestrian detection, processing methods
based on image binarization are used because the intensity (i.e. temperature) of
pedestrians is higher than that of background objects. However, at daytime or in bad
weather, making assumptions on the intensity is not always effective because of
environmental influences on FIR images (Table 2).
Image 11: Flow chart of pedestrian detection algorithm

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Table 2: Features of FIR images
4.1.3.1 Contour-based candidate area extraction
The method of contour-based candidate area extraction uses the intensity difference
between a pedestrian and the background and a constrained condition of distances to
pedestrian body parts. The constrained condition is based on the assumption that the
distance between FIR camera and each pedestrian body part (head, arms, torso, and
legs) is the same. However, the contour of a pedestrian is not always a continuous line
and is usually disconnected at every parts of the body. Therefore, the candidate area
extraction method is made of two steps: (1) Extraction of body part areas, and (2)
grouping of body part areas, as shown below.
(a) Extraction of
pedestrian body part
areas

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Image 12: Extracted contour (Prewitt operator)
First, the contours of the pedestrian and the back-ground are extracted from the FIR
image (Image 12). In consideration of the cases of bad weather where blurred images
are obtained, the Prewitt operator is used so that smooth contours can be extracted.
Next, neighbouring contour points are connected and contour groups are constructed.
The reason of this process is to prevent the occurrence of errors at the following
disparity segmentation process. The
constructed contour groups may include the contour points that belong to different
objects, so a contour group is divided into several blocks (ex. 4x4 pixels, Image 13),
and then reconstructed after being judged whether or not these blocks belong to the
same object. In the judgment process, each block’s disparity (depends on the distance
from FIR camera, as shown in Image 14) is calculated by using stereo cameras, and
the blocks subjected to below conditions are classified into the same group.
Disparity difference between the blocks is within a certain range.
Blocks belonged to the same contour group before being divided.
The last step of body part area extraction is the expansion of contour groups. Contour
points may not always be extracted on all boundaries between pedestrian and
background, so the areas of contour groups need to be expanded to the areas that
belong to pedestrian and do not involve contour points. The expansion process has
three steps: a) Set the blocks around the contour group area, b) calculate the disparity
of each block, and c) unite the block with the contour group if the block has the
disparity that is nearly equal to that of the contour group.

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Image 13: Divide of contour group
(b) Grouping of pedestrian body part areas
After the pedestrian body part areas were extracted, the body part areas are grouped
to extract the candidate area, which is equal to the entire body of the pedestrian. This
process has two steps: Unitizing and proving. In the unitizing process, the pedestrian
body part areas of equal disparity are unitized and the candidate area is generated.
Next, in the proving process, the spatial disparity among the body part areas in the
candidate area are calculated, and judged whether or not it is equivalent to the spatial
disparity of the surrounding areas. The reason this process is performed is that some
candidate areas may consist of “several” objects which are adjacent to each other and
have disparities that are nearly equal. Therefore, if the spatial disparity of the body
part areas is different from that of the surrounding areas, the candidate area is divided
into parts before being unitize.

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Image 15: Grouping of body part area using disparity information
4.1.3.2 Candidate area classification
Candidate area classification is a process of judging whether or not the extracted
candidate area is a pedestrian. In order to reduce the occurrence of classification
errors due to occlusion or video noise, this process consists of two steps: The current
frame classification and the time-series classification, as shown below.
(a) Classification in current frame
First, each candidate area is divided into several body part areas, such as head and
legs, and the “plausibility” is calculated for each body part area. The candidate area is
judged whether or not it is a pedestrian using the calculated plausibility. Then, in
consideration of the variation of images due to weather and time of the day, the
judgment is performed in accordance with environmental conditions. For example,
when extracting a head area at night time or in bad weather, image binarization is
used because a head usually has a higher intensity than the background. However, this
method is not useful in the daytime because the sun heats the background and the
intensity of the head area decreases. Therefore, the head area extraction using image
binarization is performed only at night time or in bad weather, and at daytime the

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head contour consisting of contour points is used. Parameters such as binarization
threshold are derived statistically in consideration of environmental conditions.
(b) Time-series classification
This process uses the results of classification in current and past frames, using the
tracking process. A candidate area is judged as a “pedestrian” only when the ratio of
the frames the candidate area is judged as a “pedestrian” in total frames exceeds a
certain value.
4.1.3.3 Candidate area tracking
Candidate areas are tracked over time so that candidate area classification can be
performed. In the candidate area tracking process, the similarity between the
candidate areas in the current and last frames is calculated. If the similarity is larger
than a certain level, then these candidate areas are labelled as the same. In calculating
the similarity, parameters such as candidate area size variation and gravity difference
are used. In addition, when the difference of gravity is calculated, the coordinates of
the candidate area are corrected by calculating the yaw and pitch angles of the car.
4.2 INTELLIGENT VISION FOR AUTOMOBILES AT NIGHT (IVAN)
Car driving is a process of which the safety heavily relies on drivers’ accurate visual
information processing and proper reactions. Objects such as road signs, warnings
and lane lines are critical for aiding drivers to understand the road conditions.
Failures in recognizing these objects may cause serious consequences. Practically,
drivers may experience more difficulties in identifying these objects during the night
driving, leading to a much higher probability of traffic accident. Statistics shows that,
more than 20% of fatal traffic accidents occurred between midnight and 6:00 in the
morning, which accounts for only 2.4% of total traffic volume. Besides the drivers’

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lacking of attention, largely reduced visual acuity and field of vision at night due to
low illumination caused by factors such bad weathers, obscure street lamps and
limited range of headlights is also a major reason for this situation. For example,
dipped headlights only illuminate about 56 meters when the breaking distance at 100
km/h is about 80 meters.
Facing this problem, attentions have been attracted to the research of automobile
night vision systems which help to improve the visibility of objects on the road at
night. In general, such a system is equipped with night visors such as infrared
cameras from which the information of objects presenting on the road, such as
bends, poles, pedestrians, other cars etc. can be extracted.
Then, this system will inform drivers by means of visual, acoustic or other signals
about the obstacles appearing in their way. Some of the research results have been
transformed
into real products installed on high-end automobiles such as BMW 6 Series Coupe
and Mercedes-Benz 2007 S-Class series.
Intelligent Vision for Automobiles at Night (IVAN), is a highly advanced form of night
vision system, which focuses on detecting, illuminating and recognizing road signs at
night. Infrared cameras are adopted to tackle the problem of low visibility at night.
Computer vision techniques, such as image enhancement, object detection and
recognition etc., are used intensively in IVAN to analyse videos captured by the
infrared cameras. Road sign detection and recognition functions are implemented to
reduce the probability of missing traffic signs in dark environments. The system can
be operated by the driver through a touch screen and audio notifications are used for
informing the driver of the possible dangers.

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Image 16: System Overview of IVAN
Unlike normal cameras, the infrared cameras are sensitive to infrared and, therefore,
it captures objects that reflect infrared. Figure 2 compares the images captured by an
infrared camera and a common webcam in the same night driving scenario. The
analogue video signals are first encoded using a TV capture card. Then, the video is
enhanced and pre-processed for later stages. The enhanced image is ready for shape
detection which locates possible road signs in the video frames. All the detected
shapes will be sent to road sign recognition module to check whether they
correspond to the known road signs stored in the database. If a road sign is
recognized, it will be displayed on the screen. At the same time, IVAN will alert the
driver when an important road sign, such as a danger warning, is found. The detected
shape will be displayed on the screen so that the driver will be able to move the
spotlight to illuminate the corresponding area.

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Image 17: Images from different cameras

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To adapt with different driving requirements, there are four major functionalities in
IVAN.
Adaptive night vision – IVAN captures the front view of the vehicle with an
infrared camera and displays the video onto a touch screen. Meanwhile, the infrared
camera will adaptively change direction if the vehicle is turning. The camera
automatically adjusts to the best angle so that it always captures the front view.
Road sign detection and recognition - The detection module firstly detects road
signs by processing the image captured. From these detected signs, the recognition
module recognizes important ones, such as warning signs and regulatory signs. For
recognized signs, a clearer picture will be displayed beside each one to increase the
awareness of drivers.
Spotlight projection - Once a road sign has been detected and selected by the
user, the system immediately finds its corresponding position and projects light onto
it using a spotlight mounted on a rotating platform. Automatic tracking is also
implemented in IVAN. The spotlight will illuminate on the selected road sign while
the vehicle is moving.
Scene zooming - The user is able to view road signs at long distance using the
zooming function. The user can control the degree of magnification easily by sliding
on the touch screen.
4.2.1 WORKING OF IVAN
The road sign detection module locates and segments potential road signs in real-time.
Based on the observation that most of the road signs are in regular geometric shapes,
such as rectangle, triangle and circle, the following steps are used for road sign
detection in IVAN. The input image is first processed to reduce the noise by using a 5x5
Gaussian filter. Shades of grey are then converted to black and white (binarization)
using different thresholds. For each segmented image thus obtained, contours of the
white regions are extracted. The contours are approximated into polygons by using

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Douglas-Peucker algorithm, which recursively find out a subset of vertices that the
shape enclosed is similar to the original one. The resultant polygons approximated are
further analysed: In order to improve detection speed and accuracy, they are classified
into “quadrilaterals” and
“triangles” by polygons' vertex number.
Their interior angles are then calculated.
Candidate road signs are selected from
the detected shape by checking their
interior angles. For quadrilaterals, the
interior angles should be within the
range 90 degrees; for triangles, the
interior angles should be within the
range 60 degrees. The parameters are
constants which are defined to offer tolerances to deal with the perspective distortion
and noises in the frame captured. Shapes will be discarded if they do not have
three/four vertices respectively or their interior angles violate the rules defined
above. Consequently, a set of quadrilaterals and triangles are detected, these shapes
are regarded as traffic signs and recorded by the tracking algorithm of the detection
module.
Image 18: The IVAN system
For round road signs, after the contours are extracted, the program verifies the
detected contours by matching their shapes with the ellipse computed. If more than a
half of the points are matched locally, the candidate ellipse becomes verified. During

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the process of
extraction, a geometric
error is tolerated for
each point. The degree
of the toleration varies
adaptively on the size
of each ellipse. Figure
4 illustrates the ellipse
verification process.
Image 19: The ellipse verification process
In order to stabilize the detection result while minimizing the false acceptance rate, a
tracking mechanism is employed to follow the road signs detected in the captured
videos. A circular buffer is created for each traffic sign successfully detected, the
bounding rectangle and center point are recorded in the corresponding circular
buffer. In the next frame, when a shape detected in similar location, the same circular
buffer will be used, and its bounding rectangle and center will be updated. Only the
shapes that appear in more than 5 times in 10 consecutive frames are considered as
“successful detections” and display
onto the screen. Consequently,
erroneous detections will be
eliminated, since they cannot be
detected in consecutive frames.

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Image 23: Adaptive infrared camera
The night vision feature is implemented by utilizing an infrared camera to capture the
front view. Since infrared camera has strong sensibility against infrared, the captured
images enable drivers to see the road conditions and identify road signs or other
objects at night. Inspired by BMW 7 Series’ Adaptive Headlights System, an adaptive
control mechanism is implemented by estimating an adjustment angle from the
vehicle’s speed and turning angles. Figure 8 illustrates the usage of camera
adjustment.
Image 24: Sample cases of road sign detection
4.3. TRUE-COLOUR NIGHT VISION
Numerous studies have shown that scene understanding, reaction time, and object
identification is faster and more accurate with colour imagery than with monochrome

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imagery. Considering surveillance, reconnaissance, and security applications, colour
imagery has two main benefits over monochrome imagery. The first is that colour
improves contrast, which allows for better scene segmentation and object detection.
This contrast improvement can apply to both true-colour and false-colour images,
where false-colour imagery can be formed by the fusion of images from cameras with
different spectral sensitivity (e.g., image intensified with thermal IR). The second
benefit of colour is that it provides more information. Access to stored colour
knowledge in the brain or a computer database can be utilized to enable better object
identification and scene understanding. This second improvement applies primarily to
true-colour images, since false-colour images do not necessarily match the stored
colour information, and may in fact be detrimental in this regard.
General benefits and drawbacks of true-colour night vision (TCNV) systems are listed
in Table 1, and examples of the utility of true-colour information are shown in Figure
1. For example, Figure 1 demonstrates that successfully finding the man with the
orange shirt, determining the difference between flags, or being able to pick out the
blue car are all tasks that benefit greatly from the additional information that true-
colour imagery provides.
To obtain true-colour images a camera must be sensitive to the visible portion of the
electromagnetic spectrum and there must be a mechanism to filter or split the
different parts
(i.e., colours) of the visible spectrum so that colour information can be extracted.
This need to filter the input has the consequence of reducing the available signal to a
detector, which is the primary drawback of a true-colour system intended for use in
low-light situations.
Furthermore, standard monochrome image-intensified systems are typically
designed to take advantage of the relatively high near-infrared (NIR) signal
available from the night sky. To mitigate the inherent reduction in signal due to
filtering, a true-colour system should also be able to utilize this NIR light. In
addition, sensitivity to NIR is also needed for viewing of IR laser aiming devices, as

AUTOMOBILE 2016
demonstrated in Figure 2. The ability to produce true-colour content, while
maintaining sensitivity to NIR is one of the inherent challenges in making a viable
true-colour night vision camera.
New camera technology and image processing routines have been developed to
enable the use of true-colour information from the visible portion of the spectrum
while utilizing the full visible to near infrared (V-NIR) range (roughly 400 to 1000
nm in wavelength) for the brightness information. Two different types of TCNV
cameras are there; one camera uses a liquid crystal filter in front of an image
intensified detector and the other uses a mosaic filter deposited on the pixels of an
EMCCD detector. Both cameras are based on new technologies: the liquid crystal
camera uses fast switching filters with optimized transmission bands, and the
mosaic filter camera relies on recent advances in CCD technology

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True Colour Night Vision (TCNV)
Benefits (compared to monochrome) Drawbacks (compared to monochrome)
More information – better object Reduced signal.
recognition, better scene Increased cost.
understanding.
Improved contrast – better object
detection, better scene
segmentation.
Table 3: General benefits and drawbacks of true-colour night vision cameras as to
compared standard monochrome low-light-level or image intensified cameras.
Image 25: monochrome and colour low light level imagery. The images illustrate the
additional information that is available with the inclusion of colour.

AUTOMOBILE 2016
Image 26: Image taken with TCNV camera demonstrating the ability to produce
colour imagery while utilizing both visible and NIR signal for brightness. The bright
spot on the red car is from an NIR laser aiming device.
4.3.1. DESCRIPTION OF CAMERAS
4.3.1.1LIQUID CRYSTAL FILTER INTENSIFIED CAMERA
Liquid crystal (LC) filters consist of stacks of polarizing, bi-refrainment, and variable
retardance substrates. With applied voltages, the transmission of the stack can be
electronically switched to a different band pass or “colour” state (see Figure 3). A full
colour image is constructed by using separate images taken in 3 or 4 different colour
states and then mixing them with appropriate weights to form an RGB output image.
Although the colour information is built up over multiple exposures, the image is
updated with each
captured frame, rather than waiting until a complete set of 3 or 4 frames is captured.
In addition to the visible wavelengths, the LC filters also pass NIR radiation to
increase
the available signal and to enable viewing of IR laser aiming devices. With the use of

AUTOMOBILE 2016
specifically tailored band pass states and optimized colour mixing algorithms, the NIR
signal contributes to the brightness of an image without destroying the true
colour
information.
Image 27: A liquid crystal filter shown in 3 different colour states. The colour is
switched
by changing the applied voltages.

AUTOMOBILE 2016
Fast Switching Liquid Crystal Filter Camera
Benefi
ts Drawbacks
Full colour resolution at each pixel.
Time-sequential image capture:
it
Filter can be positioned out of
takes multiple frames to produce
a
optical path for full detector full colour image.
sensitivity at lowest light levels. Reduced signal: filters rely on
Versatile: filter can be used with polarization, which leads to an
any type of VNIR low-light overall reduction in signal of
detector.
approximately 50% — (the
average
Low power.
transmission is less than 50% in
the
No moving parts.
visible, but higher than 50% in
the
Fast-switching LC: no “dead-time”
NIR)
Table 4: lists the main benefits and drawbacks of a night vision cameras that use such
LC
filters.
The latest LC filters are extremely fast switching taking less than 1ms to switch
between any two states. Fast switching enables the camera to operate without “dead-
time” and the associated light loss while the filter is in an undefined state. With typical
LC filters it is impractical to operate at video rates, i.e., 30 frames/second (fps), since
the dead-time is on the same order as the frame period. However, with the fast-
switching filter, rates as high as 180 fps are routinely used.
True colour night vision cameras use an image intensified CMOS detector with a
“smart camera” digital media processor (DMP).

AUTOMOBILE 2016
The image intensifier is a Gen III blue-enhanced tube, which is bonded to the sensor
via a 2:1 fibre-optic reducer. The CMOS array is a ½” format 640×480 pixel detector
capable of
200 fps at full resolution. A high frame rate detector is used to enable a reduction in
the image blur associated with time-sequential image capture; however, at the lowest
light settings, longer exposure times (and thus lower frame rates ~ 30 fps) are used.
Image 28: TCNV prototypes with LC filter and image intensified CMOS
6. Applications
These are the common applications of night vision technology
1. Military
2. Hunting
3. Security

AUTOMOBILE 2016
4. Navigation
5. Wildlife observations
6. Hidden object detection
The original purpose of night vision was to locate enemy target at night. It is
extensively by the military for that purpose, as well as for navigation and targeting.
Police and security often use both thermal imaging and image enhancement
technology, particularly for surveillance. Hunter are use this to detect the animals and
any other birds. Detectives and private investigator use night vision to watch assigned
to track.
Many business have permanently-mounted cameras equipped with night vision to
monitor surroundings. A real amazing ability of thermal imaging, is that it reveals
whether an has been distributed , it can show that the ground has been dug up to bury
something, even if there is no obvious sing to the naked eye. Law enforcement has
used this to discover items that has been hidden by the criminal, including money,
drugs and bodies. Also recent changes to area such as walls can be seen using thermal
imaging, which have provided important clues in several cases. Many people are
beginning to discover the unique world that can be found after darkness falls.
7.CASE STUDY
 INTERNATIONAL CASE STUDIES

AUTOMOBILE 2016
BMW : BMW Night Vision introduced in 2005 on the BMW 7 Series (E65). This
system processes far infrared radiation, which minimizes non-essential information
placing a greater emphasis on pedestrians and animals, allows for a range of 300
meters or nearly 1,000 feet, and avoids "dazzle" from headlights, road lights and
similar intense light sources.
2008 update added pedestrian detection system on the redesigned BMW 7 Series
(F01), which flashes a caution symbol on the navigation/information screen
and automotive head-up display when it detects pedestrians.
2013 update added Dynamic Light Spot.
2013 update added animal detection. The system provides a real-time video image
that also depicts on the Control Display persons, animals and other objects emitting
heat when they are outside of the light beam and warns in the event of an impending
collision. The Dynamic Light Spot is produced by a special headlight that directs the
light beam onto the recognised persons or animals respectively, thus drawing the
driver’s attention to possible hazards in good time. As soon as the remote infrared
detects pedestrians or larger animals on course for collision in the dark, the system
directs two separately controlled Dynamic Light Spots at them without creating an
unpleasant glare. In the event of an acute risk, an acoustic warning signal is also
sounded and the brakes are set to maximum standby. For the model year 2014, BMW
5-series will also have these new features.
 MERCEDES-BENZ

AUTOMOBILE 2016
Night View Assist prototype was shown in 2003 on the Mercedes-Benz F500concept.
Series production Night View Assist system introduced in 2005 on the
redesignedMercedes-Benz S-Class (W221). It was the first system to use the
instrument cluster's LCD as a display.[5]
2009: Night View Assist Plus added a pedestrian detection function calling the
revised system on the redesigned Mercedes-Benz E-Class (W212) and refreshedS-
class,[6]
however, the E-class uses the navigation screen's display.
2011: Night View Assist Plus with Spotlight Function premiere: the Mercedes-Benz
CL-Class (C216) became the first series production car with night vision-
guided pedestrian spotlighting
Night View Assist infrared projector visible next to Bi-Xenon HID main headlight,
Mercedes-Benz S-Class (W221).Mercedes-Benz has unveiled an auxiliary spotlight
feature for its so-called Active Night View Assist headlamps to provide what it
describes as “an enhanced level of pedestrian safety”.
Until now Active Night View Assist has used an infra-red camera to record ghostly
video of pedestrians within a pre-determined field ahead of the car and subsequently
play it in real time on a monitor within instrument binnacle - thus alerting the driver
to a potential safety hazard at night or in low light conditions.
The new feature, which is designed to work at speeds above 45kph, sets out to provide
not only the driver but also pedestrians with an enhanced warning by employing a
spotlight to illuminate the area where the camera detects their presence. The spotlight
feature relies on the existing infra-red camera mounted within the headlamp assembly
to detect pedestrians at distances of up to 80 metres and uses the main beam function
of the headlamps to light up the immediate area where they are detected. Depending
on the existing speed, pedestrians can be illuminated up to four times before the car
arrives .

AUTOMOBILE 2016
A second camera mounted within the windscreen, where it also assists the functions
for Mercedes’ Speed Limit Assist and Lane Keeping Assist, records the position of other
cars and determines whether it is safe to illuminate the area where pedestrians are
detected. If the headlamps are set to dipped beam, the pedestrian is illuminated with
the spotlight function beyond the field of the dipped beam.
Image: Figure showing windscreen projection
6. CONCLUSION
Automotive Head up display is an emerging technology which has many advantages
on the ergonomic aspects as well as for the comfort of driver. Researches are going on

AUTOMOBILE 2016
for the development of HUD(HEAD UP DISPLAY) to minimise the space of the central
console and displaying all the necessary information in the windshield itself. But HUD
has some limitations, that for HUD, it requires a partial reflecting element to reflect
the projected image in the windscreen to act as a screen, also the projector must be
arranged with projection angle above the critical angle of the glass to reflect it image.
Another, one main problem is that HUD cannot provide a better display during the
day time. The background light is so high that the projected image won't be properly
seen. This become challenging and limits the display area of HUD to a small portion of
the windshield.
But, HUD is more preferable for the night drive. During night, except the high beam of
approaching vehicles, high intensity lights are lesser. So HUD can work well for the
night vision. Presently, in night vision technology, after spotting a human or animal in
the field of driving, the information is displayed on the small screen on the central
consol. This is ergonomically, not completely satisfactory for the drive, as he need to
take away his eyes from the road to have a look at the screen. So, mostly drivers won't
relay on the night vision all the time. With the use of holographic glass projection
technology the vision system can be developed to a next generation, with the
combination of IVAN technology and pedestrian detection with holographic
projection the exact position, size and type of the object detected can be show directly
on the windshield glass were the driver see through. The high intensity holographic
laser projection can display the symbols detected by the IVAN as well as the road
markings on the wind screen. Also by using this projection the boundary box of the
human or animal can be shown in the wind screen at the right position of the object
which the driver could see through the windscreen.
REFERENCES
1. K. Rumar, Adaptive illumination systems for motor vehicles: Towards a more
intelligent headlighting system, Report no.UMTRI-97-7. Ann Arbor, MI: The
University of Michigan Transport Research Institute, 1997.

AUTOMOBILE 2016
2. P.A. Thompson, Daytime running lamps (DRLs) for pedestrian protection ,
Proceedings of Progress in automotive lighting, Darmstadt, Gerrmany, 2003.
3. H. Nanda and L. Davis, “Probabilistic Template Based Pedestrian Detection in
Infrared Videos,” in Procs. IEEE Intelligent Vehicles Symposium 2002, June 2002.
4. Procs. IEEE Intelligent Vehicles Symposium 2002, June 2002.
5. Y. L. Guilloux and J. Lonnoy, “PAROTO Project: The Benefit of Infrared Imagery for
Ob-stacle Avoidance,” in Procs. IEEE Intelligent Vehicles Symposium 2002, June
2002.
6. B. Heisele and C. Wohler,¨ “Motion-based Recognition of Pedestrians,” in Procs.
IEEE Intl. Conf. on Pattern Recognition, pp. 1325–1330, June 1998.
7. R. Cutler and L. S. Davis, “Robust real-time periodic motion detection, analysis and
appli-cations,” IEEE Trans. on PAMI, vol. 22, pp. 781–796, Aug. 2000.
8. M. Bertozzi, A. Broggi, T. Graf, P. Grisleri, and M. Meinecke, “Pedestrian Detection in
Infrared Images,” in Procs. IEEE Intelligent Vehicles Symposium 2003, June 2003. in
press.
Www.Wikipedia.Org
En.Wikipedia.Org/Wiki/Night_vision_device
Www.Morovision.Com/How_thermal_imaging_works.Htm
En.Wikipedia.Org/Wiki/Night_vision

AUTOMOBILE 2016
BIBLIOGRAPHY
http://www.pspc.dibe.unige.it/~drivsco
http://www.bmw.com/com/en/newvehicles/6series/coupe/2007/allfacts/ergonomics_nightv
ision.html
http://www.mercedesforum.com/m_35841/tm.htm
http://www.gps4us.com/news/post/Windshield-projection-technology-
renders-GPS-navigation-route-for-safer-driving-20111221.aspx

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