Project Report without.docx

Human Crowd Counting
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Poornima College of Engineering, Jaipur, B. Tech., Department of Advanced Computing
Human Crowd Counting Using Deep Learning
A Project report submitted in partial fulfillment of the requirements of
the award of the degree of
Bachelor of Technology
in
Artificial Intelligence and Data Science
by
Ankit Singh Chauhan,
PCE21AD300,Naina Chibermalani
PCE21AD03, Riya Navlani
PCE21AD046
under the guidance of
Dr. Mithlesh Arya, Associate Professor
(Session 2022-23)
Department of Advanced Computing
Poornima College of Engineering
ISI-6, RIICO Institutional Area, Sitapura, Jaipur – 302022
1 December 2022

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Department Certificate
This is to certify that Mr. Ankit Singh Chauhan, registration no. PCE21AD300, of the Department of Advance
Computing, has submitted this project report entitled “Human Crowd Counting Using Deep Learning” under
the supervision of Dr. Mithlesh Arya, working as Associate Professor in the department of Advance Computing
as per the requirements of the Bachelor of Technology program of Poornima College of Engineering, Jaipur.
Dr. Mithlesh Aya Ms. Archika Jain
Dy. Head, Dept. of Advanced Computing Coordinator-Project

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CANDIDATE’S DECLARATION
I hereby declare that the work which is being presented in this project report entitled “Human Crowd Counting
Using Deep Learning” in the partial fulfillment for the award of the Degree of Bachelor of Technology in
(Artificial Intelligence and Data Science), submitted to the Department of Advance Computing, Poornima
College of Engineering, Jaipur, is an authentic record of my work done during the period from July 2022 to Dec
2022 under the supervision and guidance of (Dr. Mithlesh Arya).
I have not submitted the matter embodied in this project report for the award of any other degree.
Signature Signature
Name of Candidate: Ankit Singh Chauhan
Registration no.: PCE21AD300
Name of Candidate: Riya Navlani
Signature Signature
Name of Candidate: Naina Chibermalani
Dated: -
Place: Jaipur
SUPERVISOR’S CERTIFICATE
This is to certify that the above statement made by the candidate is correct to the best of my knowledge.
(Signature)
Dated: (Dr. Mithlesh Arya)
Place: Jaipur (Dy. Head, Dept. of Advanced Computing )

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ACKNOWLEDGEMENT
I would like to convey my profound sense of reverence and admiration to my supervisor Dr. Mithlesh Arya, Dy.
Head Department of Advanced Computing, Poornima College of Engineering, for her intense concern,
attention, priceless direction, guidance, and encouragement throughout this research work.
I am grateful to Dr. Mahesh Bundele, Director of Poornima College of Engineering for his helping attitude and
keen interest in completing this dissertation in time.
I extend my heartiest gratitude to all the teachers, who extended their cooperation to steer the topic toward its
successful completion. I am also thankful to the non-teaching staff of the department to support in the preparation
of this dissertation work.
My special heartfelt gratitude goes to Dr. Nikita Jain, Dy. Head, Department of Computer Engineering, Ms.
Archika Jain, Project Coordinator, Department of Computer Engineering, Poornima College of
Engineering, for unvarying support, guidance, and motivation during the course of this research.
I would like to express my deep sense of gratitude towards the management of Poornima College of Engineering
including Dr. S. M. Seth, Chairman Emeritus, Poornima Group, and former Director NIH, Roorkee, Shri
Shashikant Singhi, Chairman, Poornima Group, Mr. M. K. M. Shah, Director Admin & Finance, Poornima
Group, and Ar. Rahul Singhi, Director of Poornima Group for the establishment of the institute and for providing
facilities for my studies.
I would like to take the opportunity of expressing my thanks to all faculty members of the Department, for their
kind support, technical guidance, and inspiration throughout the course.
I am deeply thankful to my parents and all other family members for their blessings and inspiration. Last but not
least I would like to give special thanks to God who enabled me to complete my dissertation on time.
Ankit Singh Chauhan, Department of Advanced Computing, PCE21AD300
Naina Chibermalani, Department of Advanced Computing, PCE21AD33
Riya Navlani, Department of Advanced Computing, PCE21AD46

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LIST OF FIGURES
S.
No.
Fig.
No.
Description
Page
No.
1 1 Cross-count network diagram 12
2 2 Human counting by the sensor 12
3 3 Flowchart of human detection 22
4 4 Architecture of crowd counting system 22

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LIST OF TABLES
Serial
Number
Figure
Number
Description Page
Number
1 11 Comparative study 19

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LIST OF ACRONYMS
Serial
Number
ACRONYM FULL FORM
1 RBF Radial Basic Function
2 HSI Hyperspectral Image
3 BPO Business Process Outsourcing
4 WIFI Wireless Fidelity
5 LPG Liquefied Petroleum Gas
6 HOG Histogram Of Oriented Gradients
7 SVM Support Vector Networks
8 AI Artificial Intelligence
9 CSI Channel State Information
10 RSS Residual Sum of Square

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Table of content
S. No. Title Chapter Name Page No.
1 Chapter 1 Introduction 11-13
2 Chapter 2 Problem Statement & Objective 14
3 Chapter 3 Literature Review 21
4 Chapter 4 Proposed Approach 22
5 Chapter 5 Conclusion & Future Scope 23
6 References 24

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Abstract
Human detection nowadays has become a necessary part of our lives from a security
point of view as well as for our ease. The more increasing the world population, the
higher the use of detection of the crowd will be required. In this paper, we have
combined all the techniques and methodologies proposed by different review papers.
Some of the papers have proposed the techniques of different approaches such as
feature-based, trajectory-based, map-based, and hardware-based; also in some of them
color image processing is being used to count the pedestrians; some of the papers have
proposed the technique of counting the passed people using a stereo camera, some of
the detection has been done using different gases, different algorithms of counting
human are discussed, the human ability to count verbally in discrete quantities is
unique as compared to animal cognition, some have used Wi-Fi to detect humans;
some of them has used three different cameras to detect humans. There are several
approaches and strategies used to determine the human count. Human count sounds
like a simple task, but it is more complex than it seems. It is mainly done for crowd
safety and to create routes for humans or crowds to exit different areas in case of
emergency. It can also be used to predict the size of future gatherings. From a business
point of view, analyzing or estimating the expected crowd identifies a further analysis
of demand and supply and thus allows business owners to be more equipped. Human
detection is mostly used in public places and high-security areas such as malls,
temples, hotels, clubs, institutions, airports, railway stations, etc.

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CHAPTER 1
INTRODUCTION
As we all know, due to the rapid increase in population, India is the second largest
population country,and as the population increases crime rates also getting increase
day by day due to overpopulation.
So this increase in population leads to security threats due to the reason human
counting becomes more and more important for our security. For crowd detection,
there are many methods to detect the crowd with better accuracy and efficiency, but
now it has become the most challenging task.
India is a country that has the second-largest population in the world, and it's facing
many problems as a result. Overpopulation, poverty, ethnicities, and multiple religions
are all contributing to the rise in crime. The increase in population leads to security
threats because of this reason, now, more applications for counting the crowd are
needed for public security affairs. Security-related applications allow greater
networking of cameras, greater field of vision, and cheaper access and come with a
host of tools like facial recognition and vehicle tracking. The video surveillance
market is witnessing extremely large growth in areas including manufacturing, BPO
[Business Process Outsourcing] retail, airport security, hospitality, city surveillance,
college campus, companies, and shopping malls.
There are two main categories of methods for counting people: detection-based and
measurement-based crowd-counting techniques. Detection-based methods use an
algorithm that detects regions where there is a large amount of activity (i.e., lots of
people), which can then be used to estimate the number of individuals in those regions
(i.e., how many people are there). Measurement-based methods use data from video
sequences that were collected over time by sensors placed around an area where
crowds gather (such as at a football stadium).
Human counting is a crucial part of many pervasive applications, including smart
guiding in museums, energy management in smart buildings, indoor analytics, and
people evacuation during emergencies. The problem of detecting human presence is
one of the recent most widely researched topics. Surveillance is one of the major
applications of this research, and it has led to the development of a variety of sensing
devices that can detect a human being in an open space area. These devices include
acoustic sensors, infrared detectors, chemical sensors, etc. Most of these technologies
are based on video surveillance [2]. Sensor-based technologies need to be able to
detect human presence in absence of both factors. However, all these

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Figure 1. Cross-count network diagram
Sensors-based systems or devices are normally deployed in a visible place, which
suffers from a risk of being deactivated by intruders. As a remedy for this problem,
human sensing devices should be deployed in a hidden place at an inaccessible
location within a secured area. This implies that the sensing devices need to be
responsive to some intangible signal that change due to human presence.
There are many available methods for counting people in a given area, but most rely
on the use of WIFI or Bluetooth signals. These methods are inaccurate and unreliable,
and can only be used once to obtain an accurate count.
Figure 2. Human counting by the sensor
In this work, we propose to perform human counting in a closed indoor setting using
different environmental parameters such as carbon dioxide, liquefied petroleum gas
(LPG), nitrogen dioxide, Sulphur dioxide, temperature, and humidity. We show how
the values of these parameters get changed with the change in the number of humans
present in a practical setting.
This research makes use of the HOG and SVM-based crowd counting technique to
count the number of people in a given area. The HOG-based algorithm was used to
determine the density of people as well as their speed. The SVM-based algorithm was
used to determine how likely it is that each person is a pedestrian.
The results of this research show that both algorithms worked very well in finding out
how many people were present in a particular area at any given time. In addition, they
also showed that both algorithms can detect pedestrians when they are present in
higher numbers than non-pedestrians. We perform several experiments including
finding correlation and regression between the outputs of gas sensors and the number

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of humans present. Subsequently, we apply machine learning algorithms to our
collected data sets.

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CHAPTER 2
PROBLEM STATEMENT & OBJECTIVE
In this paper, we present the method for counting humans. we assume that there is a
moving window when the pedestrian enters a specific place the moving window
detects the human face and accounts for the people very accurately.
But the problem with this method is it can’t count the human in the crowd so we have
discussed another method to reduce this problem we can use a crowd counting sensor
which has 99% accuracy to detect the human in the crowd very clearly.
This is the most advanced people-counting sensor on the market, The Ultima AI, brings
all your people-counting needs in one powerful sensor, with bi-directional people
counting, age & gender recognition, queue management, zone analytics, and
live occupancy.
CHAPTER 3

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LITERATURE REVIEW
We have discussed some of the research paper’s techniques in our review paper. In
this paper [3], we have searched for a human counting system that is how to detect
humansignals. we propose the method CROSS COUNT an accurate deep-learning-
based human count estimator that uses a single Wi-Fi link to estimate the human count
in anarea of interest. An accurate human counting system based on recurrent neural
networks. When we have implemented and value of cross count multiple test beds
shows that it can achieve a human counting accuracy to within a maximum of 2
persons 100% of the time. We don’t need any laboratory to count the humans in a
specific space this Wi-Fi connected everyone's devices so that it can detect all the ns
there and that is free wi-fi so it will get automatically connected. This system provides
different techniques for handling new challenges found in the literature such as
through-wall signal weakness, lab labor-intensive collection, imbalanced training
data, high training overhead, a high number of data links, and unavailability of CSI
data in commodity devices, and the main idea is to process the Wi-Fi link blockage
inter-arrivals rather than depending on the statistical features extracted from RSS
values.
In this paper [4], we have to detect the humans by keeping the sensor device from
humaneyesight and it is the most challenging task to keep the sensor device hidden.
This challenge is yet to be overcome as the existing human sensing devices need to be
deployed in some visible place. To solve this problem, we propose a new methodology
to predict human count sensing environmental parameters in closed indoor settings.
To do so, we use different environmental parameters (Carbon dioxide, LPG, Nitrogen
dioxide, Sulphur dioxide, temperature, and humidity) to detect human count in closed
indoor settings. With the help of these gases, sensors are made.We use different
machine-learning classifiers to detect human counts in closed indoor settings. In
addition to using different machine learning classifiers, we leverage sensor-based
technologies to sense the different environmental parameters needed for human
counting. With this technology, more consumption of energy will be there because of
sensors. our methodology is also expected to work with as low energy consumption as
possible. Thus, we investigate maximizing the level of performance or data fidelity
while minimizing energy.
In this paper [5], for human detection, we have approached three cameras and we have
analyzed approaches of camera performance in which the density of people, lightning
conditions and, occlusions, are there. After analyzing these different methods, it can
be observed that the Histogram of the gradients approach is the most robust, even when
itisn’t trained specifically for specific camera orientations. Its performance in the

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presence of occlusions is very good and hence makes it a good candidate for such
applications and this is used like a normal camera even after this. There is an RBF
kernel that helps to improve the performance than using a linear kernel now there is a
third camera that is HOG based approach with SVM that suffers from poor response
time, if it is inside the layer then its approach is better and if it is outside the layer,
then it will give poor performance. And through this paper, we analyze the techniques
to detect humans easily and very fast and accurately, by sensing them accurately. And
for this, we need a faster human detection algorithm and do not need complex training
and classification. So it can be used in the time system and from this paper, we have
learned that we have to make a faster and easy human detection algorithm.
In this paper[6], computer vision technology, image preprocessing, and human
contourdetection, all algorithms are used in counting humans in an elevator.
Counting humans based on computer vision is becoming a popular field of study
which is of great significance for saving manpower and physical resource
nowadays. Here two types of technologies are used to count humans in the elevator:
infrared detection and image processing. The infrared sensors can count the
direction and number of passengers passing in and out of the elevator. But it has
disadvantages when the elevator is crowded, it is not able to count, but human
counting in an elevator can beachieved when it is crowded through morphological
processing. After image preprocessing operations, pattern noise can be eliminated,
image quality can be improved, and image processing can prepare for the
subsequent human body detection and counting and improve the recognition
accuracy.
In this paper [7], algorithms for counting humans are discussed, and the human
ability tocount verbally in discrete quantities is unique as compared to animal
cognition. The exact evolutionary origin of the counting algorithm is not understood.
Here non-human primates exhibit a cognitive ability that’s algorithmically and
logically similar to the human counting method. Non-humans are counted by the
number or quantity when there is a choice, they choose the item which outnumbers
the other one. Non-humans did not use numbers or words like a, b, c’s ‘1, 2, 3’ to
count anything. Many researchers from different universities have proved that non-
humans usequantity or outnumbered systems. Here the method is used as monkeys
were presented with a food choice task in which two food caches that were away
from each other, with 1 to 8 food items, allowed the monkey to choose food by
touching it. And as a result, both monkeys choose the larger quantity on the majority
of trails. As expected, the monkey’s discrimination abilities are modulated by the
numerical ratio between the choices. More surprisingly, the monkey’s behavior
occurred at the moment leading up to the final choice, they slowly moved from the
first choice to the second choice and the experimenter waited to finish baiting in the
second choice, here no requirement is needed for the monkey to make a fast
decision.

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In this paper [8], Detecting human beings accurately in a visual surveillance system is
crucial for diverse application areas including abnormal event detection, human gait
characterization, congestion analysis, person identification, gender classification, and
fall detection for elderly people. The detection process generally occurs in two steps:
object detection and object classification. Object detection could be performed by
background subtraction, optical flow, and spatiotemporal filtering. The object
classification methods could be divided into three categories: shape-based, motion-
based, and texture-based. Background subtraction attempts to detect moving objects
from the difference between the current frame and the reference frame in a pixel-by-
pixel or block-by-block fashion. Under the assumption of brightness constancy and
spatial smoothness, optical flow is used to describe the coherent motion of points or
features between image frames. For motion recognition based on Spatiotemporal filter
analysis, the action or motion is characterized via the entire 3D spatiotemporal data
volume spanned by the moving person in the image sequence.
The more increasing the world population, the higher the use of surveillance cameras
will be required. Existing earlier research works are clustered into four groups:
(1) Feature-based counting approach
(2) Trajectory clustering-based counting approach
(3) Map-based counting approach
(4) Hardware-based counting approach
In Feature-based counting approaches, this method involves preparing the detector
with visual objects to search for and count all in the scene. Trajectory clustering-based
counting techniques attempt to study the centroid distances among objects. On the
other hand, Map-based counting techniques attempt to study the automatically related
mapping of low-level features with the whole number of people in the frame or inside
a frame region. Hardware-based counting approaches are dependent on the hardware
devices, which leads to cost expensive. This paper [9] discusses a lot of previous
methodologies, algorithms, approaches, or frameworks of people crowd counting
systems. These methodologies and approaches are still necessary to develop a system
to handle the issues of the massive crowd, illuminations, various variations, and heavy
occlusions on both static and dynamic crowd counting for all kinds of environments.
This paper aims to highlight the development of a new system for less computational
cost and efficient time with good performance for the researcher.
Based on color image processing, an automatic bi-directional counting method of
pedestrians through a gate is proposed. In the developed technique, one color video
camera is hung from the ceiling of the gate with a directly downward view so that the
passing people will be observed from just overhead. Firstly, the passing people are
roughly counted with the area of people in an image. The moving direction of the
pedestrian can be oriented by tracking each person’s pattern by analyzing its HSI
histogram. This paper[10] presents an automatic bi-directional passing-people

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counting method based on color image processing. An area-based strategy is adopted
for deriving a rough estimate of pedestrians. The color information of the pedestrian’s
clothing or wear can be utilized to verify such an area-based counting. To reduce the
general overlapping, a camera setting with a directly downward view is adopted.
Another overlapping problem mentioned previously can be solved by introducing the
analysis of hue or intensity histogram.
In this paper [11], we propose an automatic method of counting the passing people
throughthe gate by using a stereo camera. In the proposed method, the stereo camera
is hung from the ceiling of the gate and the optical axis of the camera is set up so that
the passing people could be observed from just overhead. Because in this system
arrangement, if there is a crowd of people at the gate, then the image data of the passing
people are not overlapped each other on the obtained images. In addition, each height
of the passing people is met by applying the algorithm based on triangulation. In this
paper, we describe the algorithm for counting the passing people and show some
experimental results obtained by a simple experimental system to verify the
effectiveness of the proposed method. Stereo images on measurement lines are
transformed into space-time image which includes the data of passing people. But each
moving direction of the passing people could not be recognized from one space-time
image. In this system, each moving direction of the passing people is detected by
applying the process of template matching. In addition, the heights of each person are
obtained from the stereo images.

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Table 1: Comparative study:-
S.No. Paper title Author’s
Name
Year Approach used Finding S/w and H/w
Required
1 People
Counting in
Elevator Car
Based on
Computer
Vision
Honghui Fan et
al
2019 Using
Background
difference and
infrared image
Number of humans
in an elevator
Infrared camera
And image
processing
2 The Origin
of Counting
Algorithms
Jessica F. Anton 2015 Using non-
humans as
subjects and
quantity
checking
How do non-
human beings
count things
No software
or hardware
required
3 Intelligent
human
counting
through
environme
ntal
sensing in
closed
indoor
settings
Kamal,
Uday,
Shamir
Ahmed,
Tarik Reza
Toha, Nafisa
Islam, and A.
B. M. Alim Al
Islam
2020
Using a chemical
sensing device
To count humans
by sensing human
fragment
Chemical sensing
device and data
analyzing
4 A deep
learning
system for
device-free
human
counting
using Wi-Fi
Shen, Zan, Yi
Xu, Bingbing Ni,
Minsi Wang,
Jianguo Hu, and
Xiaokang Yang.
2018 Using wi-fi
connection as
subject
Number of
people
connected to
wi-fi
Wi-fi
connection
and data
analyzing

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5
A
comparative
study of
vision-based
human
detection
techniques in
people
counting
applications
Raghavachari,
Chakravartula, V.
Aparna, S.
Chithira, and
Vidhya
Balasubramanian
2015 Using a camera
to analyze how
many humans
have crossed
under the camera
or door
Human count in
malls and metro
stations
The camera set
up on the roof of
the crossing part
of the security
check
6 Human
detection in
surveillance
videos and
its
applications
Manoranjan Paul*
, Shah M E Haque
and Subrata
Chakraborty
2013 Using video to
analyze the
human numbers
in a place over
some time
Human numbers in
place
Surveillance
camera and video
storage
7 Analytical
Study of
Different
Techniques
in Crowd
People
Counting
Framework
Htet Htet Lin and
Kay Thi Win
2018 It involves
preparing the
detector with
visual objects to
search for and
count all in the
scene
Human count in a
crowded place like
market
Map reading
8 An
Automatic
Bi-
Directional
Passing-
People
Counting
Method
Based on
Color
Image
Processing
Thou-Ho (Chao-
Ho) Chen, Che-
Wei Hsu
2004 Using color
image processing
process
Finding human
count from still
images
Image processor

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9 A Method
of Counting
the Passing
People by
Using the
Stereo
Images
K. Terada, D.
Yoshida, S. Oe J.,
Yamaguchi
2002 Using human size
algorithm
Human crossing
stereo camera
vision
Stereo camera,
AI with the
algorithm of
human size

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CHAPTER 4
PROPOSED THE
APPROACH
1. Detection-Based Method –
In this method, we use a window-like-detector to count humans from an image
that how many humans are there. This method of detection required well-
trained classifiers that can extract low-level features. Although this method is
good for detecting faces, it doesn’t work well in crowded places.
2. Regression-Based Methods-
This method is used by, the first crop the patch from the image, and then for
each patch that is cropped, it extracts the low-level features.
3.Density Estimation-Based Methods-
In this first, we create a density map for the objects. After that algorithm studies
a linear mapping between the extracted features and their object density maps.
Fig 3: Flowchart of human detection Fig 4: Architecture of crowd counting system

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CONCLUSION & FUTURE SCOPE
Here, In this paper, we have discussed about the methods of counting people. Here we
have come across the Ultima AI counter technique and window detection method.But
in the window detection method, accuracy becomes very low. But compared to Ultima
AI, with the help of an AI sensor, we can count people even in a crowd and the
accuracy of the Ultimate Sense Accounting method is 99%.
Therefore, the proposed solution can be a great tool to prevent intruders in a secured
indoor space.
At the end of this paper, a discussion is made to point out the future work needed to
improve the human detection process in surveillance videos. These include exploiting
a multi-view approach and adopting an improved model based on localized parts of
the image
Much better algorithms can be prepared for better performance. With more databases,
more images can be learned. Cheaper components can be proposed. The project's
future scope is creating a much wider database, i.e., with a larger space that can
recognize more human faces with much more precise algorithms.

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References
[1] Bansal, A. and Venkatesh, K.S., 2015. People counting in high-density crowds
from still images. arXiv preprint arXiv:1507.08445.
[2] Perng, J.W., Wang, T.Y., Hsu, Y.W. and Wu, B.F., 2016, July. The design and
implementation of a vision-based people counting system in buses. In 2016
International conference on system science and engineering (ICSSE) (pp. 1-3). IEEE.
[3] Shen, Zan, Yi Xu, Bingbing Ni, Minsi Wang, Jianguo Hu, and Xiaokang Yang.
"Crowd counting via adversarial cross-scale consistency pursuit." In Proceedings of
the IEEE conference on computer vision and pattern recognition, pp. 5245-5254. 2018.
[4] Kamal, Uday, Shamir Ahmed, Tarik Reza Toha, Nafisa Islam, and A. B. M. Alim
Al Islam. "Intelligent human counting through environmental sensing in closed indoor
settings." Mobile Networks and Applications 25, no. 2 (2020): 474-490.
[5] Raghavachari, Chakravartula, V. Aparna, S. Chithira, and Vidhya
Balasubramanian. "A comparative study of vision-based human detection techniques
in people counting applications." Procedia Computer Science 58 (2015): 461-469.
[6] Fan, H., Zhu, H., and Yuan, D., 2019, April. People count in elevator cars based
on computer vision. In IOP Conference Series: Earth and Environmental Science (Vol.
252, No. 3, p. 032131). IOP Publishing.
[7] Cantlon, J.F., Piantadosi, S.T., Ferrigno, S., Hughes, K.D. and Barnard, A.M.,
2015. The origins of counting algorithms. Psychological Science, 26(6), pp.853-865.
[8] Paul, M., Haque, S.M. and Chakraborty, S., 2013. Human detection in surveillance
videos and its applications-a review. EURASIP Journal on Advances in Signal
Processing, 2013(1), pp.1-16.
[9] Lin, H.H. and Win, K.T., Analytical Study of Different Techniques in Crowd
People Counting Framework (Doctoral dissertation, MERAL Portal).
[10] Chen, T.H., 2003, October. An automatic bi-directional passing-people counting
method based on color image processing. In IEEE 37th Annual 2003 International
Carnahan Conference on Security Technology, 2003. Proceedings. (pp. 200-207).
IEEE.
[11] Terada, K., Yoshida, D., Oe, S. and Yamaguchi, J., 1999, October. A method of
counting the passing people by using stereo images. In Proceedings 1999
International Conference on Image Processing (Cat. 99CH36348) (Vol. 2, pp. 338-
342). IEEE.

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