In recent trends, digital information to the industrial integration for the intelligent transportation system (ITS) field is gaining importance for the researcher, academia, and industrial persons. Visual information helps to manage traffic systems in the industrial forum to build smart cities in developed countries. This paper presents vision-based real-time vehicle detection and Vehicle Speed Measurement (VSM) using morphology operation and binary logical process for an unplanned traffic scenario using image processing techniques. Vehicle detection and VSM help to reduce the number of accidents and improve road network efficiency. The bounding box size for vehicle detection is flexible according to vehicles’ sizes on the road. We test this system with different colors and dimensions for a selected Region of Interest (ROI). The ROI sets using the two-line approach. Here, we compare the proposed system with the inter-frame difference method and the blob analysis method with recall, precision, and F1 performance parameters.

1. Journal of Industrial Information Integration xxx (xxxx) xxx
Please cite this article as: Janak D. Trivedi, Journal of Industrial Information Integration, https://doi.org/10.1016/j.jii.2021.100280
Available online 1 September 2021
2452-414X/© 2021 Elsevier Inc. All rights reserved.
Vision-based real-time vehicle detection and vehicle speed measurement
using morphology and binary logical operation
Janak D. Trivedi a,*
, Sarada Devi Mandalapu b
, Dhara H. Dave a
a
Assistant Professor in Electronics & Communication Department, Government Engineering College, Bhavnagar, Gujarat Technological University, Gujarat, India
b
Principal at Ahmedabad Institute of Technology, Ahmedabad, Gujarat Technological University, Gujarat, India
A R T I C L E I N F O
Keywords:
Industrial Transportation
Smart City
Vehicle Detection
Vehicle Speed Measurement
Morphology
Blob analysis
A B S T R A C T
In recent trends, digital information to the industrial integration for the intelligent transportation system (ITS)
field is gaining importance for the researcher, academia, and industrial persons. Visual information helps to
manage traffic systems in the industrial forum to build smart cities in developed countries. This paper presents
vision-based real-time vehicle detection and Vehicle Speed Measurement (VSM) using morphology operation and
binary logical process for an unplanned traffic scenario using image processing techniques. Vehicle detection and
VSM help to reduce the number of accidents and improve road network efficiency. The bounding box size for
vehicle detection is flexible according to vehicles’ sizes on the road. We test this system with different colors and
dimensions for a selected Region of Interest (ROI). The ROI sets using the two-line approach. Here, we compare
the proposed system with the inter-frame difference method and the blob analysis method with recall, precision,
and F1 performance parameters.
1. Introduction
The industrial revolution is necessary for economic development.
Industrial integration with digital transformation makes the required
step for development in rural and urban areas. As per the industrial
revolution in the 19th
century [25], use of machines is increased over
human or animal power in the agriculture revolution. Better metals and
fuel are also contributed to industrialization, which powered factories,
machines, and crafts. Roads, canals, and roadways are changed
dramatically and allowing goods to be sent over long distances. Visually,
the revolution was evident in the new industrial towns, with smoking
factories dominating the skyline. The cities were horrible to live in
because it was overcrowded, dangerous conditions in the factories, and
the lifestyle of citizens.
The automotive industries go from purely vertical to more horizontal
with other partners—partnerships in many sense valuable to grow
automotive industries. For example, Audi had an excellent H.D. map
which is helpful for other companies like BMW that is good for driving
sense and location-based services. Safety and security are the biggest
concerns, and different industries are trying to deal with them wisely,
like sharing data with a competitor in an intelligent transportation
system industry. Any endeavor does not want car accidents, whatever
the competition is like google, apple, amazon competing. However, they
help and support each other in a more meaningful and more extensive
ecosystem.
Industrial work attracts people to the cities to such an extent that in
1750 only 15% of Britain’s population lived in towns; by 1850, over
50% of Great Britain lived in a city, and by 1900, it was 85%. Similar
kinds of situations in other countries. The United States is the world’s
leading industrial nation in the 20th
century [25].
The future of the city is a smart city. It is entirely interconnected,
which regulates traffic, saves energy. The world’s largest population
lives in cities, and it is steadily rising, which results in enormous chal­
lenges. People increasing traffic also increase, resulting in more pollu­
tion, energy consumption, water usage, and waste. Smart cities are
supposed to solve these problems. The continued growth of the urban
population has necessitated the creation of smarter cities for the twenty-
first century. Although progress has been made in this area over the past
two decades, city planners have been forced to seek an alternate form of
smart cities due to ongoing challenges. Recent technological advances
have aided this process in 5G communications, blockchain, and virtual/
augmented reality. In [65] Bohloul, include an analysis of existing smart
city definitions and components. It also addresses recent events, market
prospects, and recent trends. Smart city projects are already developed
* Government Engineering College Bhavnagar, Vidhyanagar, Bhavnagar 364002, Gujarat, India.
E-mail addresses: trivedi_janak2611@yahoo.com (J.D. Trivedi), saradadevim1@gmail.com (S.D. Mandalapu), dave.dhara24888@gmail.com (D.H. Dave).
Contents lists available at ScienceDirect
Journal of Industrial Information Integration
journal homepage: www.sciencedirect.com/journal/journal-of-industrial-information-integration
https://doi.org/10.1016/j.jii.2021.100280
Received 22 April 2020; Received in revised form 12 May 2021; Accepted 30 August 2021

2. Journal of Industrial Information Integration xxx (xxxx) xxx
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in different countries, including Nigeria, South-Korea, India, Malaysia,
U.S., Switzerland, Japan, China, and many more.
Smart cities, as imagined by tech companies and urban planners with
(1) Smart lighting: regular streetlights replaced by intelligent poles. (2)
Smart mobility: driverless car (3) Smart logistics: drones and robots
deliver goods, even coffee (4) Smart harvest: salad grows underground
at the urban farm (5) Smart help: augmented and virtual reality make
the process more efficient, for example, firefighters on duty can be
supported by the control center, and technology helps to find and correct
system error to prevent injury before it happens. However, for these
ideas to become a reality, technical preconditions are essential [64][76].
The cities’ challenges are changing rapidly with finite resources of
clean drinking water, highway systems with traffic congestion, air and
noise pollution, and many more. Chen [12, 23] reviews different
research categories from various research publications in Industry In­
formation Integration Engineering (IIIE) from 2006 to 2019. The author
reviews IIIE into aerospace, agriculture, automated factory, biology,
chemical engineering, construction, disaster, ecosystem, energy, enter­
prise integration, environment, general engineering, geology, health­
care, information and communication technologies (ICT), industrial
control, instrumentation, and measurements, large industrial projects,
life science, machinery, management, manufacturing, math modeling,
marine transportation, mechanical industry, medical pharmaceutical,
military, microbiology, mining, navigation, pedestrians, supply chain,
security, telecommunication, transportation, urban development, and
warehousing. Smart healthcare system by smart devices for regional
medical union designed to support doctors from different hospitals to
access health condition for the patient is explained in [66] by Xu, Li, Hu,
Wu, Ye, and Cai.
1.1. Industrial transportation engagement
Intelligent Transportation System (ITS) development with a combi­
nation of industrial integration significantly impacts human life. In­
dustry and transportation engagement build the future scope of the
automotive industry for intelligent driverless vehicles system. The
autonomous vehicle system helps to accidental avoidance and removes
carbon emission and transportation noise problems. The latest devel­
opment of the transportation system gears up for the latest improvement
of the industrial revolution. Digital technology and the internet of things
(IoT), including machine learning and deep learning technology,
enhance the ITS. The computer vision-based traffic management in­
cludes vehicle detection, vehicle counting, vehicle speed measurements
(VSM), smart parking system, automatic incident detection, and many
more in the lists.
1.1.1. Industrial city and transportation
According to McNulty [27], the industrial primary noise source is
transportation, which needs to frame with antipollution laws. The
improvement in vehicle quality requires vehicle designers, town plan­
ners, legislators, and environmentalists. Egidi, Franco, Gigliola, and
Aniello [28] report an accident risk due to transportation in Italy’s
populated area. Duke and Chung [29] evaluate pollution prevention
measures to reduce pollutants. The authors further mention different
activities to reduce stormwater pollutants. Pill, Steinbauer, and Wotawa
[30] present a compositional model for online diagnosis of transient
faults like malfunctioning transportation segments, misrouting, and
sensor errors in industrial transportation systems. Harris [56] and
Walcott [57] discuss industrial cities and industrial parks. Carter, Adam,
Tsakis, Shaw, Watson, and Ryan [67] have discussed the importance of
pedestrian mobility to develop smart cities.
Lindsey, Mahmassani, Mullarkey, Nash, and Rothberg [32] explore
the interest of transportation planners, economic development special­
ists, and private industry for industrial demand and transportation ac­
tivities. They have used regression techniques to find the relationship
between transportation activity and industrial space for the
metropolitan area. Song, Wang, and Fisher [33] report that trans­
portation may promote or constrain industrial structure development in
China. In that, environmental-oriented quantitative analysis is used to
find the impact of developing transportation on different industries.
Qiu and Huang [36] discuss interactive decision-making between the
supply hub in the industrial park and its member in transportation ser­
vice sharing. They share transportation services beneficial to manufac­
ture, the environment, and the supply hub in industrial parking. Janak,
Sarada Devi, and Dhara [71–74] have explained an intelligent parking
system for real-time application. Wang, Zhu, and Yang [48] investigate
transportation infrastructure and industrial agglomeration have affected
China’s industrial energy efficiency. Based on panel data of China’s 30
provincial-level regions from 2000 to 2017, they have applied the
threshold panel model to verify the nonlinear relationship between
transportation infrastructure and industrial energy efficiency.
Lu, Minoru, Zhaoling, Huijuan, Yong, Zhe, Tsuyoshi, Xiaoman, and
Yuepeng [62] present an Urban-industrial symbiosis (UIS) strategy,
which represents effective ways to reduce carbon emission in the city.
Sun and Hu [63] proposed a framework to inspect employee conve­
nience applied to other economic development impacts, especially in the
labor market.
1.1.2. Carbon emission impacts due to transportation and industries
Chiu, Flores, Martin, and Lacarriere [35] explain mobile thermal
energy storage for industrial surplus heat transportation for
low-temperature district heating networks. The portable thermal energy
storage evaluated the environmental impact of CO2 emissions due to
transportation. Manzone and Calvo [38] analyze the energy necessities
and the CO2 emission of wood chip transportation in a short supply
chain using two different types of vehicles: agricultural and industrial
convoys. Truck and tractor efficiency for dry road and the versatile road
is checked.
Costa, Rochedo, Costa, Ferreira, Araújo, Schaeffer, and Szklo [40]
applying the Kernel Density function in a geographic information sys­
tem. The CO2 industrial emissions reduction method could reach up to
68% in Portugal and 74% in Spain.
Das and Roy [41] analyze the multi-objective environment to reduce
total transportation cost, transportation time, and carbon emissions
from existing sites. Resat and Turkay [43] present a multi-objective
mixed-integer programming problem for integrating specific synchro
modal transportation characteristics. While optimizing the proposed
network structure, the issue includes different objective functions,
including total transportation cost, travel time, and CO2 emissions. .
Traffic congestion, time-dependent vehicle speeds, and vehicle filling
ratios are considered, and computational results for different illustrative
cases are presented with real data from the Marmara Region of Turkey.
Li, Xu, Wang, Zhang, and Yu [47] have analyzed the amount of CO2
transfers by the two countries, the United States and China, caused by
final consumption and its structural distribution from 1993 to 2013. The
critical domestic sectors in both countries were the Electricity, Gas, and
Water sector, the Transportation sector, the Petroleum, Chemical, and
Non-Metallic Mineral Products sector, which accounted for 20 to 30% of
the total.
Dong, Song, Ma, Zhang, Chen, Shen, and Xiang [54] select six major
industrial sectors, including agriculture, industry, construction, trans­
portation, retail and accommodation, and other sectors, as a research
object for the understanding of the relationships among carbon emis­
sions, the industrial structure and economic growth in China. Feng, Xia,
and Sun [55] explore structural and social-economic determinants of
China’s transport CO2 emissions from 2004 to 2016 using the loga­
rithmic mean index. Zheng, Gao, Sun, Han, and Wang [58] propose
two-dimensional difference relations for studying the influencing factors
of regional carbon emission differences based on the Quadratic Assign­
ment Procedure model.
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1.2.3. IoT in the transportation system
There is a considerable investment in an intelligent transportation
system. In the business-to-business world of transformation, digital
technology is shaking things up [26]. Anaza, Kemp, Briggs, and Borders
[46] investigates stories about buyer-seller experiences in digital tech­
nologies. In the current scenario, a disrupter business is developing
real-time predictive maintenance solutions for traffic congestions, ac­
cident detection, and vehicle speed measurements.
When the industry talks about autonomous driving, they cooperate
with intel, mobile A.I., with Tier one suppliers. It is a platform business,
and it is fun to see how all these different players are working together
on new challenges. The digital interface includes customer interface, car
product and services, and its production. The different automotive in­
dustries use more technology and robots to support their people and
employees in the factories to do the heavy lifting, sort out things, and
transport items. Robots are interconnected in an industry. The printing
and designing of the parts made by an Artificial Intelligence (A.I.) al­
gorithm can develop a piece that never should break.
IoT plays a crucial role in accessing data on vehicles. The commercial
advantages for including IoT for ITS are identifying the best driver
pattern over the same route and reducing energy consumption by
coaching drivers on the best driving practices. Another advantage of IoT
in the autonomous vehicle system reduces human effort and reduces
traffic accidents by collecting real-time data in the unsupervised traffic
road. Digital technologies are changing transportation in many different
ways. Ianuale, Schiavon explain the impact of urban networks in the
metropolitan area, and Capobianco [68] in light of the functions of
networks referred to transportation systems and ’big data’ associated
with them. Then they have measured the impact of both transportation
and big data networks, establishing their centrality and addressing the
current needs.
It is essential first to understand that there is no global regulatory
framework for collecting and using data. That depends really on national
jurisdictions and multinational jurisdiction, as in the case of Europe. The
general data protection regulation came into force in May 2018. Each
data is ownership stamped to delete from a particular driver from a
specific user.
1.1.4. Autonomous transportation system
Franke and Lutteke [31] present an automated guided vehicle (AGV)
for law payload with low-cost onboard sensors. The camera systems
continuously track all the static and movable obstacles. This versatile
autonomous vehicle is highly flexibles with different industrial appli­
cations. Provotorov, Privezentsev, and Astafiev [34] outline industrial
enterprise operation for the automatic checking system for industrial
product movement in all production process phases using radio fre­
quency identification (RFID).
Trentesaux and Rault [37] explain the importance of cyber-physical
industrial designs for humans’ welfare interacting with these systems
and their possible responsibility for an accident-like situation. The
development of the cyber-physical system in the transportation system
illustrates these recommendations. Mollanoori and Sabouhi [42] paper
develop a new mathematical model for a capacitated substantial step
fixed-charge transportation problem. The problem is formulated as a
two-stage transportation network and considers shipping multiple items
from the plants to the distribution centers (D.C.) and afterward, from D.
C.s to customers.
Bonassa, Cunha, and Isler [44] propose a mixed-integer program­
ming formulation to answer a variation of the Dynamic Multi-Period
Auto-Carrier Transportation Problem. The objective is to find the best
combination of vehicles to be loaded on auto carriers over a
multiple-day planning horizon, such that the total transportation cost is
minimized. Computational results on a set of problem providers in Brazil
show that applying the mathematical model while considering the dy­
namic nature of the problem yields cost savings and reduces the number
of vehicles delivered with delays.
Garrido and Sáez [45] and Campos, López, Quiroga, Manuel, and
Seoane [50] present a framework of automatic generation of industrial
digital twins which is suitable to support preliminary design phases of
systems. That support for the next steps of detailed designs imple­
mentation and systems running stages.
Wang, Yuan, Wang, Liu, Zhi, and Cao [61] present the effect of
disease (Covid-19), which reduces the number of on-road vehicles and
diminishes factory production. Automatic vehicle detection helps to
reduce traffic congestion.
Rabbani, Sadati, and Farrokhi [51] describe an industrial waste
transportation system in the automotive industry by proposing a ca­
pacitated location routing model with a heterogeneous vehicle fleet.
This research attempted to demonstrate the model efficiency for car
Companies in Iran to decide the route of collection, the location of
collection centers, the reduction of the costs, the risk posed to the
population, the categorization of transportation of different waste types,
and the estimation of the number of vehicles in the transportation phase.
Draganjac, Petrović, Miklić, Kovačić, and Oršulić [60] present a novel
method for highly-scalable coordination of free-ranging automated
guided vehicles in industrial logistics and manufacturing scenarios.
1.2. Digital transformation to intelligent transportation system
In the past few years, with computer vision techniques, the trends
and work culture in the traffic management system with the help of IoT
is a hot research topic. Automatic traffic management using visual in­
formation has been an active research area. The visual information-
based traffic management includes vehicle detection, vehicle counting,
vehicle speed measurements (VSM), smart parking system, automatic
incident detection, and many more in the lists. Vehicle detection is the
first and essential step required for the automated counting of vehicles
and reduces traffic congestion at intersection points. Using the VSM
system, notice can be sent to over-speed vehicle users, which helps to
improve the traffic management system by controlling the number of
accidents, road network efficiency improves.
Vehicle detection can be done using either software or hardware. The
hardware-based vehicle detector method works with an inductive loop
and laser detector, whereas the software-based vehicle detector works
with different image-processing techniques. The hardware-based tech­
niques require an additional installation, whereas the software-based
methods use an available video sensor (surveillance camera system) in
the urban areas. The filtering operation is required for object detection
and tracking movable object. The filtering operation is divided into two
categories for computer vision. In the first filtering operation, data
transfer from a spatial domain to a frequency domain can be performed
using Fourier or any other transformation. The other method of filtering
deals with spatial domain filters, like directly process pixels in the im­
ages. The second approach requires less computational complexity than
the first approach.
In computer vision fields, the inter-frame difference (IFD) and
background subtraction (B.S.) methods are used in many articles for
different applications. Celik and Kusetogullari [4] present an automated
surveillance system using video technology. Adinarayana, Sirisha,
Krishna, and Kantikiran [5] implemented speed-protected vehicle
detection systems using LabVIEW software.
The background subtraction method is divided into three groups:
pixel-based, region-based, and frame-based. In the Gaussian Mixture
Model (GMM), kernel densities are well-known pixel-based background
subtraction methods. The non-parametric kernel density, Statistical
Circular Shift Movements (SCSM), Principal Component Analysis (PCA)
methods are region-based background subtraction methods. The real-
time adaptive traffic light control system with the help of vehicular
density value is demonstrated by Janak, Sarada Devi, and Dhara [78]. In
this study, the frame-based B.S. method is used. The frame-based
background subtraction method provides better results compared to
the pixel-based background subtraction method.
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1.3. Industrial implementation
The compliance demands some investment as, of course, does
research and development. Transport systems are changing rapidly, like
to predict vehicle route in advance by observing the patterns. With the
help of machine learning, different vehicles can analyze this data and try
to find new patterns. If it achieves engineers with much fewer head­
aches, transportation is probably cheaper to run and more efficient.
Transportation would become more competitive.
Burandt, Xiong, Löffler, and Oei [39] describe three possible decar­
bonization pathways to scrutinize different effects on electricity, trans­
port, heating, and industrial sectors until 2050. The growing population
and increasing demand for energy require energy transformation.
Cheng, Yang, Gen, Jang, and Liang [52] present the importance of
machine learning (ML), deep learning (DL), and reinforcement learning
(R.L.) for the 4th Industrial revolution. In the field of transportation:
massive data is collected and used to optimize route selection, taxi
dispatching, dynamic transit bus scheduling, and other mobility services
to improve the efficiency of the operations. Younan, Houssein, Elhoseny,
and Ali [49] presented a comprehensive review of challenges and rec­
ommended technologies for the Industrial IoT. Priyanka, Maheswari,
Thangavel, and Bala [59] resent research work focusing on developing
Integrated IoT based intelligent architecture to perform online moni­
toring and control the pressure-flow rate in the fluid transportation
system.
Pitakaso, Sethanan, and Jamrus [53] address the vehicle routing
problem with consideration of vehicle capacities, time windows, mul­
tiple products, fleet sizes, and fleet size limits on roads using hybrid
particle swarm optimization and adaptive large neighborhood search
algorithms for software and mobile application for transportation in the
ice manufacturing industry. Finogeev, Fionova, Lyapian, and Lychagin
[69] have presented the development and implementation of the com­
ponents of an intelligent monitoring system to collect and process big
data on road incidents from photo-radar complexes for the smart road
environment. The global ITS market size was USD 1643.8 million in
2018. It is projected to reach USD 8474.2 million by 2026 [70].
1.4. Motivation
Industry and transportation relationships reduce carbon emission
with the image-video processing technologies in the transportation
system. The computer vision system helps build a smart city that benefits
industrial towns and industrial parks in urban areas. Autonomous ve­
hicles need adaptability, which can be developed using ML or DL with
IoT.
The ITS provides a seamless journey, full of convenience and pre­
mium aspects from integrated services for smart cars. If the industry
does not incorporate all these different users’ travels, they stay mono­
lithic, and they are not that premium and convenient. The world comes
together, so we see a significant change in society. The industry should
no longer look at country barriers or the planets because they live in the
same boat and come closer together. Digital systems play a considerable
role in the social network because companies are also social. It will new
normal, and the best companies will also be very successful in this
different future. There will be a significant shift in autonomous cars, and
transportation is more competitive than today in crowded areas like
urban cities. Digital technologies are revolutionizing the whole ITS. It is
not about products and services because what is clear is the digital
innovation is changing the very nature of business-to-business
relationships.
Some articles motivate work with the flow for a particular applica­
tion like vehicle detection and VSM in ITS. The vehicle speed (km/h)
using an improved three-frame difference algorithm and optical flow
value is measured in Lan, Li, Hu, Ran, and Wang [9]. The method gets
contour information through ’Dilate,’ ’Difference,’ and ’XOR’ opera­
tions. The local and global optimum threshold value is obtained using
the mean and standard deviation of whole images. The method’s limi­
tation is that the VSM error is significant when the vehicle speed is too
fast or slow. The combination of big and small vehicles makes a false
calculation of the VSM for the selected ROI. In this study, that limitation
is optimized to overcome in the proposed method.
Kumar and Kushwaha [11] present VSM using a single camera in the
daytime (the properly illuminated environment). Vehicle detection and
tracking use different parameters such as position, width, the height of
vehicles. We use this information for higher robustness and efficiency of
vehicle detection and speed measurements.
1.4.1. Research-gap
A variety of research publications are available for vehicle detection
and VSM in the field of ITS. The current research is still working with the
real-time implementation of vehicle detection and VSM in urban areas.
The successful implementation of ITS requires vehicle detection in
various conditions. Single vehicles on the road, more than one vehicle
with different colors, have the same speed and size passing through the
selected ROI. The implementation of this method requires lower
installation as well as maintenance costs. In this study, the present work
is tested with different environmental conditions. We implement a
current system for vision-based real-time unplanned traffic conditions.
1.4.2. Main objective
The main objective of this work is to develop vision-based real-time
vehicle detection and VSM for ITS in the smart city. This method helps to
improve traffic management systems using available surveillance cam­
eras in urban areas. The system helps to reduce traffic accidents, traffic
congestion, and improved road network efficiency.
1.4.3. Organization of the article
The remaining part is organized as follows: Section 2 shows the
related work of VSM and vehicle detection for ITS improvement. This
part also indicates the future scope of each of the individual articles.
Section 3 demonstrates the method flowchart and pseudocode. In this
part, the basic morphology and logical operation are described. Section
4 and section 5 indicate results and discussion. The conclusion and
limitations of this system with the future scope are explained in section
6.
2. Vehicle speed measurements and vehicle detection for ITS
VSM and vehicle detection are the integral research part for the
development of ITS. In [4,5], vehicle detection for VSM using IFD and B.
S. is explained. The adaptive threshold is applied for the detection of a
vehicle after the implementation of IFD and B.S. The speed detection is
performed in a binary image. The proposed framework’s exactness
within the speed estimations is comparable with the moving vehicles’
actual speed. In addition to IFD and BS, Hough Transform-based VSM is
explained in Nguyen, Pham, and Song [6].
Automatic extraction of moving vehicles and determine their speeds
from a pair of QuickBird (Q.B.), panchromatic (PAN), and multispectral
(M.S.) images are discussed in Liu, Yamazaki, and Vu [7]. That method
was tested on actual and simulated Q.B. images. Two thresholds were
used from histograms to identify road and background. Then the defined
threshold helps to identify vehicles from the object. The accuracy of
vehicle extraction from Q.B. images is less than the simulation results.
The future study verifies the accuracy of vehicle extraction.
Inwon, Pil, Eun-Ju, Chi-Hak, and Kim [8] present the two cameras
based VSM. One camera is used for a telescope view, and the other one is
used for a comprehensive view. That method must find a license plate
region using an upper and lower camera and calculate the license plate
region’s height for finding vehicle speed.
A dynamic background subtraction and object tracking algorithm
using Diagonal Hexadecimal Patterns (DHP) for VSM is described in
Jeyabharathi and Dejey [10]. The system performance is measured
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5. Journal of Industrial Information Integration xxx (xxxx) xxx
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using Metric F-score and Multiple Object Tracking Accuracy (MOTA).
The method’s challenging task is ready to change for scale, illumination
variations of a real-time system. The bounding box size enlarges or small
changes vehicle reference from the camera position. The future work to
consider the first-order derivative for horizontal, vertical, and diagonal
code. Yang, Li, Song, Xiong, Hou, and Qu [21] present non-intrusive
stereo vision-based VSM. That detects license plate with two video
streams using extracted stereo matching points pairs. The future target
to work with the same system installed in the vehicle. A non-intrusive
video-based VSM system comparing the tracked features’ trajectories
is demonstrated in Luvizon, Nassu, and Minetto [13]. The future work
for estimating the distance using license plate detection and applying
OCR for license plate recognition for managing traffic speed control
system. Future works also for the reduction of computational complexity
with better hardware interfacing. Farahani [14] presented object
tracking under the condition of a similar kind of background and fore­
ground color information using the mean-shift algorithm and extended
mean-shift algorithm. The VSM using one pixel’s width line processing
system with the help of B.S., morphological operations, binarization,
and Blob-detection is explained in Bourja, Maach, Zennayi, Bourzeix,
and Guerin [15]. The method helps to reduce the cost of hardware
products.
IoT and proximity sensor-based system in forest area for alerting the
driver with over-speed and vehicles nearer to animals using a buzzer is
explained by Bhagyashree, Singh, Kiran, and Padmini [16]. Gunawan,
Tanjung, and Gunawan [17] explain VSM using the Direct Linear
Transform (DLT), B.S., and Mixture of Gaussian (MoG) method. The
method has developed a prototype model for ITS using Python pro­
gramming. The different angle (degree) positions and camera position
(height in cm) for controlled environment conditions are demonstrated
for VSM. The methods are applied to the kopo-toll road for vehicle
detection and VSM. The future work for the multi-vehicle detection and
speed measurements for real-time vehicles on the road. The limitation of
the method with the use of a proximity sensor for the selection of the
range. The future work is about using a radar sensor in place of the
proximity sensor and works on an automatic brake system.
Javadi, Dahl, and Pettersson [18] present a video-based VSM system
using instruction line techniques and probability density function. The
VSM using Gaussian filter from the data extracted by multiple object
tracking methods, You Only Look Once (YOLO) and Kalman Filter from
the drone-video, is explained in Liu, Lian, Ding, and Guo [19]. Shir­
anthika, Premaratne, Zheng, and Halloran [20] and Janak, Sarada Devi,
and Dhara [75] present Vehicle Counting and VSM using Gaussian
filtering, Morphological filtering, convex-hull for a real-time road in
Australia and India. The limitation of pneumatic tubes, installed earlier
temporarily, is removed using this computer vision-based proposed
method. Another limitation of the method is that the system was not
tested in different illumination conditions, which is the direction of
future work.
The VSM without feature extraction is described in Lu, Wang, and
Song [22], which have used a frame difference method to the ROI, then
projection histogram and key bin extraction obtained for deciding
vehicle motion. The system tested on three datasets, including vehicles
with speed detection using a radar speed detector. The proposed method
does not depend on camera parameters. The system had used four
intrusion lines, and frames are captured using smartphone devices with
a rate of 30 fps and 50 fps. The future work is on selecting tracking points
automatically and tests this method with different environmental con­
ditions. The PCA and vision-based VSM, with the help of the
contour-finding algorithm, is explained by Mini and Vijayakumar [24].
The Spatio-temporal Varying Filter (STVF) is used for pre-processed
extracted frames and frame-count algorithm for VSM. The method has
measured high accuracy, recall, precision value. The experiment results
for 18 H.D. videos, each with around 1hr time duration was demon­
strated. The future work for vehicle detection using machine learning
classifiers[77] with improving accuracy and performance.
3. Method description
There are three different methods for VSM using IFD and
morphology operation discuss in this study. Method-1: 3-frame differ­
ence method [9], Method-2: Simple Blob analysis [15,20], Method-3:
Morphology, and logical operator-based propose method.
3.1. Basic morphology operation
Morphology operation is easy to use in image processing for various
applications. The various morphology operations are dilation, erosion,
opening, and closing. The different process is explained by Gonzalez and
Woods’s basic book of image processing [1]. The hit, fit, and miss
concept is required to understand various morphology operations. Hit
means some of the structuring element pixels combine with image pixels
for further computation. Fit means all of the structuring element pixels
combine with image pixels for additional analysis. Miss means none of
the structuring element pixels combine with selected image pixels for
computation.
Opening and closing are two significant mathematical morphology
operations. They are both derived from fundamental operation dilation
and erosion. These two operations typically applied to binary images,
although there is also a grey-level version. During the dilation operation,
pixels are added when a structuring element hits at least one pixel.
Dilation enlarges objects. Dilation makes the object more visible, fills
small holes in the object. Dilation of binary image A by structuring
element B is defined as per equation (1). During the erosion operation,
pixels are removed when a structuring element hits at least one pixel.
Erosion makes the object small so that only sustainable object remains.
Erosion of binary image A by structuring element B is defined as per
equation (2).
An opening is defined as an erosion followed by dilation using the
same structuring element for both operations, as shown in equation (3).
The opening is similar to erosion i which remove some foreground pixels
from the edges of the region of foreground pixels. However, it is less
destructive than erosion. The opening is the dual of the closing. Opening
the foreground pixels with a particular structuring element is equivalent
to closing the background pixels with the same element. Closing is
defined as a dilation followed by an erosion using the same structuring
element for both operations, as shown in equation (4). Closing is similar
in some ways to dilation in that it tends to enlarge the boundaries of
foreground regions in an image. Morphological closing is valuable for
filling small holes from an image while keeping the objects’ shape and
size in the image.
Dilation A ⊕ B =
⋃
b∈B
Ab (1)
Erosion AΘB =
⋂
b∈B
A− b (2)
Opening A∘B = (AΘB) ⊕ B (3)
Closing A • B = (A ⊕ B)ΘB (4)
3.2. Binary logical operation
In this study, different binary logical XOR-AND operation is used,
explained by Mano [2]. These binary logical operations are performed
using two binary or grey-level images A, B - as input and output a third
image whose pixel values result (XOR, AND) of corresponding pixels
from the input images A, B. The mathematical representation of both the
logical operation represents in equations (5), (6). XOR AND operation
are performed in a single pass, with all the input values are the same.
Here, image reading from real-time video with a fixed video resolution
of 320 × 480 pixels, so all images read the same values. These logical
operators work more reliably with binary input, then apply threshold
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6. Journal of Industrial Information Integration xxx (xxxx) xxx
6
values to these images. The stationary and movable objects can easily
detect using a combination of binary and morphological operations. The
use of these binary logical operations is expressed in [9] for VSM.
XOR A ⊕ B = AB
−
+ A
−
B (5)
AND A⋅B = AB (6)
3.3. Method description
3.3.1. Inter-frame difference method (IFD)
IFD is one of the most used computer vision methods for any appli­
cations related to image-video processing, such as object detection,
recognition, counting, segmentation. This method-based VSM is
described in [4,5,6,9]. This method difference between the ’t’ frame and
’t+1′
frame is computed for object detection. This process of object
detection is improved with a combination of Background Subtraction (B.
S.) Method. In [9], the improved 3-frame difference method is
explained. The effect of the improved 3-frame difference method is
shown in figure 1.
3.3.2. Blob analysis
Blob stands for a large binary object. A method of an image using a
binarization process is called "Blob Analysis." In image processing
techniques, blob analysis is used for the detection of selected objects/
regions. This process calculates statistics for the labeled area in a binary
image. The VSM using one pixel’s width line processing system is with B.
S., morphological operations, binarization, and blob-detection
explained in [14,19]. The binarization process is the essential step in
the image processing.Blob analysis method analyzing an image or video
with the help of the binarization process. Blob analysis is the primary
method to find an object’s features, counting the number of objects in
the picture or scene. Blob analysis can also help to find the area, posi­
tion, length of the objects. Blob represents connected pixels of the group.
When two or more pixels are connected, they find connectivity with the
help of the neighborhood concept. The 8-connectivity gives more ac­
curate results than the 4-connectivity, but in the 4-connectivity, fewer
computations are required, which process the image/video faster than
the 8-connectivity.
3.3.3. Morphology and logical operator based method
In this study, we represent VSM and vehicle detection using
morphology and logical operators. The pseudocode and system flow­
chart present in section 3.3.3. The initial step is to obtain an image from
the video sensor and select ROI with two-line approaches. The two-line
separate from each other with a measurable distance. Then apply
morphology operation. In this process, first, we have to select a struc­
turing element (S.E.). The Kalman filters [6,10,14,19] are used in our
system to track the vehicles for an unplanned traffic situation. This filter
helpful in tracking the moving object in different conditions. In the
flowchart, Method 1, Method 2, and Method 3 are inter-frame difference
methods [9], simple blob analysis [15,20], and the proposed method,
Fig. 1. Effect of improved 3-frame difference from [9].
Fig. 2. The proposed method with a flowchart.
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7. Journal of Industrial Information Integration xxx (xxxx) xxx
7
respectively. The VSM is possible after vehicle detection. So, the first
step in all the methods is to detect vehicles correctly. Then VSM is
calculated using Euclidean distance formula [1] and basic speed mea­
surement formula as indicated in equations (7) and (8).
EuclideanDistance E.D.(x, y) =
̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅
(
xi − xj
)2
+
(
yi − yj
)2
√
(7)
SpeedMeasurementSpeed(km/hr)=distancebetweentwoline∗3.6/timeinsecond
(8)
*For VSM calculation, here we assume the 200-meter distance be­
tween two selected (green line in figure 3 (c-f)).
3.3.3. Pseudocode
If video==1
Image (1, 2…) = read(video)
Adaptive Threshold=threshold value
Blob variables= {Centroid, Area, Bounding box}
If
Image Continue= (image2-image1)>threshold value
Method (1,2,3)
Morphology operation
Binary Logical Operation
Blob analysis
End
Vehicle tracking and VSM with color box
End
4.Result
The original image is captured using a fixed potion camera on the
road, as shown in figure 3(a). Then apply the proposed method with
morphology operation, and the result is shown in figure 3(b). Different
morphology operations like dilation, opening and closing operation are
performed on original image 3(a). Figure 3(c) shows the blank road
surface with a two-line approach for the selected ROI. Here, we have
assumed the 200-meter distance between two green lines. The white car
with its speed for the selected ROI is shown in figure 3(d). A similar
result obtains for a red-color vehicle, as shown in figure 3(e). The vehicle
and speed measurement detection is done for both sides using this
method, shown in figure 3(f). In figure 3(d-f), the two yellow color
numbers represent vehicle identification numbers (lower) and vehicle
speed (upper), respectively. For the robustness and accuracy of the
proposed method, the results are compared with two approaches dis­
cussed in method-1 & method-2 using performance parameters F1,
recall, and precision. The equations for the same are indicated in (9 to
11). Testing and validation of this work are done using performance
parameters F1, Recall, and Precision. These evaluation parameters are
discussed in Powers [3].
Recall = T.P./(T.P. + F.N.) (9)
Precision = T.P./(T.P. + F.P.) (10)
F1 = 2 × Recall × Precision/(Recall + Precision) (11)
Where T.P. is True Positive, F.N. is False Negative, F.P. is a false positive
indication (Fig. 2).
True positive (T.P.) mentions the number of predicted correct values.
In contrast, False positive (F.P.) refers to the number of predicted
incorrect values and similarly for True negative (T.N.) and False-
negative (F.N.). The sensitivity and confidence are measured in terms
of recall and precision and accuracy in the F1 parameter. The sensitivity
of the vehicle detection method is shown in table 1. After vehicle
detection, the VSM is calculated for the proposed method. Different
statistics are used to validate the proposed plan. The maximum, mini­
mum, and average speed is calculated for every detected vehicle. The
average error for vehicle speed detection is calculated as per equation
(12). The average error with different statistics measurements for
vehicle speed measurement is shown in table 2.
Average Error
∑
n
i=1
(⃒
⃒
⃒
⃒
Vspeed − Vavg
Tn
⃒
⃒
⃒
⃒
)
(12)
Vspeed is measured vehicle speed between two lines; Vavg is average
vehicle speed, Tn is the total number of detected vehicles.
5. Discussion
The vehicle speed and vehicle detection are done using the image
processing technique over the input image captured from the fixed po­
sition camera. This study presents vehicle detection and VSM using
morphology and binary logical operations. The bounding box size in
vehicle detection is smaller or larger, according to the size of detected
vehicles. In table 1, verification and testing are done for the proposed
system, comparing method-1 [9] and method-2 [15,20] using evalua­
tion parameters recall, precision, and F1. The accuracy of the proposed
method higher compared to both approaches. The proposed method
accuracy is 0.87, higher than 0.66 (method-2) and 0.79 (method-1), as
shown in table 1. In method 1, vehicle detection accuracy is more than
Table 1
The different videos with resolution 480 × 320, Frame rate – 25 Frames/ Second. Method-1 [9], Method-2 [15,20].
Sr. No. No. of Frames in Video Recall Precision F1
Method 1 Method 2 Presented
Method
Method 1 Method 2 Presented
Method
Method 1 Method 2 Presented
Method
1 430 0.78 0.63 1 0.78 0.78 0.72 0.78 0.7 0.84
2 496 0.7 0.7 1 0.88 0.78 0.78 0.78 0.74 0.87
3 205 0.7 0.54 0.92 0.82 0.7 0.71 0.76 0.61 0.81
4 567 0.84 0.59 1 0.84 0.78 0.8 0.84 0.68 0.89
5 205 0.86 0.72 1 0.86 0.72 0.78 0.86 0.72 0.88
6 292 0.75 0.25 1 0.86 1 0.8 0.8 0.4 0.89
7 224 0.84 0.67 1 0.63 0.8 0.75 0.72 0.73 0.86
8 630 0.75 0.55 1 0.75 0.92 0.8 0.75 0.69 0.89
Average 0.7775 0.58125 0.99 0.8025 0.81 0.7675 0.78625 0.65875 0.86625
Table 2
The video with resolution 480 × 320, Frame rate – 25 Frames/ Second. The VSM
in Km/ Hr for the proposed method.
Sr. No. Maximum Speed Minimum Speed Average Speed Average Error
1 81 68 71 3
2 99 81 86 3
3 102 84 98 4
4 77 60 66 4
5 85 67 69 5
6 90 84 85 1
7 121 97 109 2
8 110 96 106 1
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8. Journal of Industrial Information Integration xxx (xxxx) xxx
8
method-2 but lower than the proposed method. The black and red cars
are not detected in the case of blob detection (method-2). The recall
value is lower, and F.N. values are higher in that case. The white color
car is accurately detected in the same case (method-2). All three
methods can detect the different sizes of vehicles correctly. The varia­
tion of colors can handle better with the proposed method compared to
method-1 and method-2. When a big vehicle and a small vehicle are
passed together on the road, the proposed method detects both the ve­
hicles, but sometimes it generates false positive numbers.
This study can detect vehicle and VSM for both (the opposites) sides
of lanes, as shown in figure 3(f). The VSM for all three case measures
assumes that the difference between the two lines is 200 meters. In table
2, the VSM is calculated with different statistics measurements. The
false-positive number of vehicle detection increases the false detection
in vehicle speed measurement. The vehicle’s maximum speed and the
minimum speed of the vehicle differ due to the vehicle position differ­
ences for a fixed camera position. So this is the limitation of this work for
VSM in a real-time scenario.
6. Conclusion and future scope
The combination of industrial engineering with an intelligent
transportation system helps reduce carbon emission, the noise produced
due to the transportation system, and the efficiency of on-road traffic
management with an autonomous system. This paper presents vision-
based real-time vehicle detection and VSM using different morpholog­
ical and binary logical operations for an unplanned traffic scenario with
a computer vision method. The different types of vehicles cannot be
detected sufficiently in the IFD and B.S. methods. Similarly, different
colored vehicles cannot be adequately detected in blob methods. The
intended approach helps vehicle detection and VSM for different colors,
sizes, and shapes with better efficiency (recall, precision, and F1 value)
than other approaches without any additional hardware installation.
The surveillance camera can practice for vehicle detection and VSM to
develop the ITS in the smart city. So, there is a saving in maintenance
cost, which requires a sensor-based traffic management system. Vehicle
detection and VSM can reduce accidents and advancements for road
network efficiency in the traffic management system.
This study represents vehicle detection and VSM, requiring fewer
human resources with the best camera position and high camera reso­
lution. Future studies can be done for the optimization of the above case.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Credit Author Statement
Janak D. Trivedi – Corresponding Author: Conceptualization,
Methodology, Software, Validation, Writing - Original Draft, Writing -
Review & Editing, Visualization
Sarada Devi Mandalapu: Conceptualization, Writing - Review &
Editing, Supervision, Project administration, Writing - Original Draft
Dhara H. Dave: Conceptualization, Writing - Review & Editing
Funding
No funding was received for this work.
Fig. 3. (a) Original Image of the road (b) After applied morphology operation (c) Propose Two-line for VSM (d) vehicle detection and VSM for single side ’white’ car.
(e) vehicle detection and VSM for single side ’red’ car (f) vehicle detection and VSM for both side different color cars.
J.D. Trivedi et al.

9. Journal of Industrial Information Integration xxx (xxxx) xxx
9
Intellectual Property
We confirm that we have given due consideration to the protection of
intellectual property associated with this work and that there are no
impediments to publication, including the timing of publication, with
respect to intellectual property. In so doing we confirm that we have
followed the regulations of our institutions concerning intellectual
property.
Authorship
All listed authors meet the Journal of Industrial Information Inte­
gration criteria. We attest that all authors contributed significantly to
the creation of this manuscript, each having fulfilled criteria as estab­
lished by the Journal of Industrial Information Integration.
We confirm that the manuscript has been read and approved by all
named authors.
We confirm that the order of authors listed in the manuscript has
been approved by all named authors.
Contact with the Editorial Office
The Corresponding Author declared on the title page of the manu­
script is:JANAK D. TRIVEDI –trivedi_janak2611@yahoo.com
This author submitted this manuscript using his/her account in
editorial submission system.
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