In the recent era, vehicles become a need of public. According to Statistic Portal, in year 2018 alone, more than 81 million vehicles were sold. This results in a large number of vehicles commuting on roads, thus increases the risks of road users. Road safety is the paramount and joint responsibility of all road users, which include pedestrians and travellers using different means of transport. Safety is always a main concern for drivers. It is a complex and difficult task even for an experienced senior driver. Road accident is the most unwanted thing to happen to a road user; it was reported that most of the road users are familiar with the general rules and safety measures when using roads, nonetheless their carelessness are causing the accidents and crashes. Zhang et.al [1] proposed an intelligent driver assist system for urban driving. This system provided smart navigation for its users with intelligent parking assistance to improve driving comfort while ensure the safety of the driver. The investigations of the system performance showed high precisions in the determination of the traffic flow and parking availabilit

Hui Ling, Chua

Huong, Audrey

English

In the recent era, vehicles become a need of public. According to Statistic Portal, in year 2018 alone, more than 81 million vehicles were sold. This results in a large number of vehicles commuting on roads, thus increases the risks of road users. Road safety is the paramount and joint responsibility of all road users, which include pedestrians and travellers using different means of transport. Safety is always a main concern for drivers. It is a complex and difficult task even for an experienced senior driver. Road accident is the most unwanted thing to happen to a road user; it was reported that most of the road users are familiar with the general rules and safety measures when using roads, nonetheless their carelessness are causing the accidents and crashes. Zhang et.al [1] proposed an intelligent driver assist system for urban driving. This system provided smart navigation for its users with intelligent parking assistance to improve driving comfort while ensure the safety of the driver. The investigations of the system performance showed high precisions in the determination of the traffic flow and parking availability.�

Chua, Hui Ling

Huong, Audrey Kah Ching

UTHM Institutional Repository

  
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Bioengineering Principle and Technology Applications Volume 2 
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CHAPTER 3 
 
INTELLIGENT IMAGING SYSTEM FOR OPTIMAL 
NIGHT TIME DRIVING 
 
Chua Hui Ling 
Audrey Huong  
 
Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 
86400 Batu Pahat, Johor, Malaysia 
 
 
3.0 INTRODUCTION 
 
In the recent era, vehicles become a need of public. According 
to Statistic Portal, in year 2018 alone, more than 81 million vehicles were 
sold. This results in a large number of vehicles commuting on roads, thus 
increases the risks of road users.   Road safety is the paramount and joint 
responsibility of all road users, which include pedestrians and travellers 
using different means of transport. Safety is always a main concern for 
drivers. It is a complex and difficult task even for an experienced senior 
driver. Road accident is the most unwanted thing to happen to a road 
user; it was reported that most of the road users are familiar with the 
general rules and safety measures when using roads, nonetheless their 
carelessness are causing the accidents and crashes. Zhang et.al [1] 
proposed an intelligent driver assist system for urban driving. This 
system provided smart navigation for its users with intelligent parking 
assistance to improve driving comfort while ensure the safety of the 
driver. The investigations of the system performance showed high 
precisions in the determination of the traffic flow and parking 
availability.  
Driving challenges arise everywhere and anytime especially 
during the night. One of the main issues when driving at night is poor 
visibility. A driver’s view is limited to the distance illuminated by 
vehicle headlights. Furthermore, drivers do not have the advantage of 
colour and contrast vision compare to the daytime. Driving at night also 
  
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reduces driver’s ability to see the other vehicles. Even though a driver 
may have used a powerful and effective headlight on their car, its 
function is limited due to the glare of headlights from other vehicles and 
brightly lit signs or buildings. Most drivers’ eyes recovered from such 
glare in between three to five seconds or even longer [2].  
The time for the eyes of a driver to recover from glare while 
driving at night increases with age. It was predicted that 90 % of the 
drivers made their decisions based on their instinct. Under this limited 
lighting, traffic accidents, which may involve pedestrians, vehicles, 
animals and any other hazards from farther away, are likely to occur [3]. 
Driving during night is a challenging task for many due to limited and 
poor visibility at night. However, it is hard to avoid travelling at night. 
For this purpose, Luo et al. [4] proposed pedestrian detection based on 
two different sensing technologies: active night vision using near-
infrared (NIR) region of the electromagnetic spectrum, and passive night 
vision that operates with far-infrared (FIR) spectrum.  
It must also be mentioned that night vision goggle has recently 
developed as an image enhancement technology that is able to collect all 
the available surrounding light, which includes infrared light, before 
amplifying it. As a result, its users would have a clearer vision in the dark 
[5]. However, it is impractical for use in road settings owing to its size 
and weight. In addition, this device costs about RM 5,000 [6]. Meanwhile 
advanced driving assistance system (ADAS) was invented by Intel to 
assist its driver and improve one’s driving experience. Its 
primary function is to ensure the safety of the vehicle, its driver, and 
other road users. Safety features were designed to avoid collisions and 
accidents by providing alert to the driver. This technology can be 
connected to a smartphone to alert other drivers about the danger. In 
addition, it is also able to notify its users of their vehicle’s blind spot [7]. 
The limitation of this system is it provides no function for a clear night 
vision [7]. Therefore, a night vision with an advanced road safety 
technology is necessary; it would be useful to allow drivers to see and 
foresee road hazards sooner, and give them more time to react. This topic 
discussed an intelligent imaging system for optimal night time driving. 
This is via an in-car safe driving system and inter-vehicle communication 
platform for sharing of visual information using Google Cloud Platform. 
Not only does this system allow its drivers to foresee road hazards 
sooner, giving them sufficient time to react, it also allows sharing of 
  
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visual information on the interesting events specifically those related to 
either criminal activity or concerning road safety in the created network 
group. 
 
3.1 COMPARISON OF THE LATEST INVENTIONS 
 
In this sub-chapter, research papers related to this study are discussed. 
 
3.1.1 An Intelligent Night Vision System for Automobiles [8] 
 
This system used infrared cameras and computer vision 
techniques to enhance road users’ safety. The system was coined as 
Intelligent Vision for Automobiles at Night (IVAN). The operation of 
this system began by encoding analog video signals before the 
corresponding video is enhanced and pre-processed. The processed 
image would then undergo a series of shape detection analysis, which 
locates possible road signs in the video frames. The shapes recorded were 
analyzed to identify for the possible road signs. If a road sign is 
recognized, it would be displayed on the screen. At the same time, IVAN 
would alert the driver of the important road signs [8].  
 
3.1.2 Night Vision System in Bayerische Motoren Werke 
(BMW) [9] 
 
This system installed in BMW branded vehicles is known as 
night vision camera (NVC). The night vision camera is produced by 
Autoliv to detect images in the form of electronic signals. The result 
would be transferred to a Liquid crystal display (LCD) screen. The next 
generation of the system includes display of enhanced images of 
forthcoming driving scenes. In the display important objects such as 
pedestrians and animals, which has been argued to be the primary safety 
goal of night vision system, are being highlighted [9].  
3.1.3 Infrared Night Vision Based Pedestrian Detection System 
[10] 
 
A camera was set up on the driving car in this work. The input 
signal is the continuous infrared photographs recorded. An analysis was 
  
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performed for the timing and spatial conditions. It was shown that the 
system is able to detect any pedestrian around the vehicle [10].  
3.1.4 Vehicle Detection and Tracking System for Improved 
Night Vision [11] 
 
This work presented image processing, vehicle detection and 
tracking using night vision images captured by a Closed- Circuit 
Television (CCTV). It is hypothesized that this product can be used in 
crime investigation to either find vehicles that violated traffic rules or 
those involved in accident in the highway. It was found during the system 
testing that vehicle headlight’s reflection pose a major concern to the 
system’s performance. This, however, can be overcome by adjusting the 
camera settings namely luminous or brightness, contrast and intensity.  
A comparison of the reported research works is summarized in Table 1. 
 
Table 3.1: Comparison of previous developed devices  
Reference Object of 
interest 
Detector 
used 
Image 
color 
Acceptance 
angle 
[8] Road’s 
signboard 
Infrared 
Camera 
Color 60 degrees 
[9] All aspects Far 
Infrared 
Camera 
Grayscale Acceptance 
angle decreases 
with increased 
speed 
[10] Pedestrian 
only 
Not 
mentioned 
Grayscale Not mentioned 
[11] Road’s 
signboard 
CCTV Grayscale Not mentioned 
 
3.2 SYSTEM DEVELOPMENT 
Image processing is a series of computational operations on an 
image to achieve enhanced signals with improved quality. Image 
processing is used in different field with its own purpose. This work 
involves the implementation of image processing operations using 
Raspberry Pi as its microcontroller, and this platform was operated on 
  
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Google Cloud service. The integrated infrared camera was used to 
capture live video, i.e. a series of images, during nighttime driving. The 
results were enhanced to identify objects within the images that have 
been recorded. The processed streaming video was displayed on a 
monitor for reference of its user. 
The developed system saved the records of live videos in a 
Secure Digital (SD) card; these series of images were uploaded and saved 
together with its coordinate location to Firebase database shown in 
Figure 1. The detection of the location was via a Global Positioning 
System (GPS) module. The safekeeping of such information in cloud-
based storage would allow others to access. This would alert other users 
of the forthcoming dangers or hazard as shown in Figure 2. Even though 
a user may go offline, the data would keep updated.  
In addition, this system was also equipped with anti-collision 
feature using ultrasonic sensor technology to detect the distance between 
the vehicle and front object. If the detected distance is less than 500 cm, 
an alarm would be triggered to notify its user. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3.1: Block diagram of system 
Users 
GPS 
 
Video 
streaming 
(Live) 
Snapshot 
of image 
Raspberry Pi 
Google Firebase Monitor 
Anti-collision 
system 
  
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Figure 3.2: Schematic representation of system 
 
3.3 RESULTS AND DISCUSSIONS 
The section is divided to three main parts namely that of imaging system, 
data sharing system, and anti-collision system as followed.  
3.3.1  Infrared imaging system 
Infrared camera was used to take image and record live video. 
The reason of this selection is that this light wavelength range is 
unaffected by visible light. As a result, camera users can see equally well 
in both well-lit and total darkness environments [12-13]. The 
performance of the developed system was evaluated in terms of the 
calculated image Signal to Noise ratio (SNR) at different driving speed 
and the system response time. The SNR is measured in decibels (dB). 
 
Google Cloud 
Firebase 
  
GPS 
coordination and 
snapshot taken 
Foresee hazard 
Accident 
  
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According to ISO film speed equivalent, excellent image quality needs 
to have at least 32.04 dB while acceptable image quality needs to achieve 
20 dB [14]. The SNR ratio is calculated by: 
 
                                    SNR=10*10 log (𝑖𝑖𝑖𝑖𝑖𝑖−𝑖𝑖𝑖𝑖𝑖𝑖
𝑖𝑖𝑖𝑖𝑖𝑖
)                         (1)                                         
where ima is maximum pixel of image, imi is minimum pixel of image 
and ims is the standard deviation of image. 
This work investigated image SNR for driving speed of 20, 40, 
50, 70, 80 and 110 km/h. This image quality metric was evaluated from 
three sets of experiment that carried out at nighttime. The results are 
shown in Figure 3.  
It can be clearly seen that the SNR decreased with an increased 
in the vehicle speed. The SNR is determined by the object sharpness and 
the object contrast. The higher the SNR ratio, the better the quality of 
image. The image appeared blur when the speed of driving increased. It 
was shown that the maximum speed to maintain while retaining a 
relatively clear image is at 80km/h.  
 
Figure 3.3: SNR value for images taken from three sets of 
experiment at different travelled speed 
 
  
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3.3.2 Online data sharing platform 
The time taken in uploading the acquired image into cloud 
system was also computed based on trials conducted for ten consecutive 
times. The mean and standard deviation of system response time are 
defined as follows: 
 
                                 Mean = µ = 1
𝑛𝑛
∑ t𝑛𝑛𝑖𝑖=1                                      (2) 
           Standard deviation = 𝑠𝑠 = � 1
𝑛𝑛−1
� (t − µ)𝑛𝑛
𝑖𝑖=1
2                   (3) 
where μ and s represented mean and standard deviation of system 
response time, respectively, while the symbol n is number of samples. 
The calculated online data sharing response times are as shown in Figure 
4.  
Figure 3.4: The response time of the system when uploaded data to 
Firebase storage and Firebase database. 
  
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From Figure 4, the overall response time of the developed 
system is calculated as 5.6 ± 1.6 s. Meanwhile Figure 5 and 6 show the 
streaming of data into Firebase database and Firebase storage, 
respectively. Public users can foresee the location coordinates where 
alert had been reported and its associated images through the link 
provided: https://final-199013.firebaseio.com/. The rapid sharing of 
information among road users would also allow a fast track down of 
offenders’ vehicle and their whereabouts. This community cooperation 
and policing would help to speed up necessary responses by relevant 
authorities, thereof effectively minimizing criminal cases such as 
abduction and robbery. The driver may disseminate image from inside 
the car using this system to hint other road users for assistance in case of 
emergency or life-threatening events.  
 
 
Figure 3.5: Data sent to Firebase Database 
  
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Figure 3.6: Data sent to Firebase Storage 
3.3.3 Anti-collision system 
 The mean and standard deviation in the measurements of 
separation between vehicle and obstacle for the activation of the alert 
system are calculated as 440.1 ± 9.92 cm. There is a slight mean 
difference of 12 % is noticed between the set value and that measured 
distance. This study shown that the performance of the integrated anti-
collision system was able to sense and notify the user of objects or 
obstacles located at mean distance of 440 cm in front of the vehicle which 
users able to avoid life threatening incident to occur. 
 
3.4 CONCLUSION 
 
 Intelligent imaging system for optimal night time driving 
provides in-vehicle and inter-vehicle interactions by using Google cloud 
service: Firebase. This system is able to provide a near real-time 
communication platform, which response time is calculated as 5.6 ± 1.65 
s and allow road users to share the interesting information and the 
coordinate where the data was taken. The data sharing can notify other 
users in case of emergency and let them foresee the hazards on road. The 
obtained SNR results revealed that this system may not be suitably used 
for night time driving because of low quality of images taken. This may 
cause by motion artefacts of moving vehicles. This may be overcome by 
using an advanced and better performance imaging sensor that has a high 
frame rate. 
  
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REFERENCE 
[1]  Zhang, Y., and Ioannou, P., 2016. Intelligent Driver Assist   
            System for Urban Driving. Digital Media Industry and  
            Academic Forum. 128–134. 
[2]  Patorgis, C. J. Clinical Procedures in Optometry. JAMA  
           Opthalmology 110 (8): 482-486. 
[3]  National Safety Council, 2017. The most dangerous time to  
            drive. 
[4]  Luo, Y., Remillard, J., and Hoetzer, D., 2010. Pedestrian   
 Detection in Near-Infrared Night Vision System. 
[5]  Illumin, 2003. Night Vision Goggles Moving from Military    
to Modern Day Applications/ 
[6]       ATN Cooperation, 2000. Night-vision-goggles. 
[7]      Zhao, M. Y., 2015. Advanced driver assistant system. Intel Labs. 
[8] Yu, T-H., Moon, Y.S., Chen, J., Fung, H. K., Ko, H. F., and 
Wang, R., 2009. An Intelligent Night Vision System for 
Automobiles. Department of Computer Science and 
Engineering. The Chinese University of Hong Kong, Shatin, 
Hong Kong. 
[9] Ahire, A. S., 2014. Night Vision System in BMW. Mechanical 
Engineering Department, Pune University, INDIA. 
[10] Ho, C. Y., and Fang, C. Y., 2014. Infrared Night Vision Based 
Pedestrian Detection System. Department of 
Computer Science & Information Engineering, National Taiwa
n Normal University. 
[11] Rajkumar, C., and Mahendran, S. M., 2014. Vehicle Detection 
and Tracking System from CCTV Captured Image for Night 
Vision. Department of Compter Science, Madurai Kamaraj 
University College, India. 
[12] Marques, O., 2011. Practical Image and Video Processing 
Using MATLAB. 
[13] Salzer, R., 2010. Biomedical Imaging Principles and 
Applications.  
[14] Konrad, R., Dansereau, D. G, Masood, A., and Wetzstein, G., 
2017. ACM Trans Graph 36. 
 
 
 


Intelligent imaging system for optimal night time driving

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Intelligent imaging system for optimal night time driving

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