In today’s traffic world, ambulance plays a major role when an accident occurs on the road network and the need arises to save valuable human life. Transportation of a patient to an emergency hospital seems quite simple but in actuality, it is quite difficult and gets more difficult during peak hours. In our Ambulance Booking System, people can easily book an ambulance. There are three major modules namely User, Ambulance, and Hospital. Users can register and log in using credentials. Users can edit their profile and change their password in an emergency. Any Upcoming Ambulance Booking details if anyone wants to Book an Ambulance or if there is an Emergency. For booking an ambulance users have to select ambulance size, pick-up point & hospital, and date & time. In an emergency will automatically book the nearest ambulance & hospital. Users will get a list of All the bookings of Ambulances. The front-end involves Html, CSS, and JavaScript and the back-end involves Python. The framework used is Django and the database is MySQL. In this system, there are four entities User, Ambulance and Hospital. The user must register and log in using a username and password. After logging in, the user can Book Ambulance, Book Hospital, View Nearby Hospitals, View Previous Booked Ambulances and Hospitals, and it can also change its password. When the user books an ambulance and hospital, a booking request is sent to the respective representatives of the ambulance and hospital. In view, Nearby Hospitals the user can view the nearest hospitals in their location. The ambulance driver has to register and then login in using a username and password. After logging in, the driver can view booking requests, nearby hospitals and their previous bookings i.e., previously accepted requests. In Booking requests, it can either accept or decline the user requests. The hospital has to register and log in using a username and password. After login in the hospital representative can view the booking request and either accept or decline the user request.In today’s traffic world, ambulance plays a major role when an accident occurs on the road network and the need arises to save valuable human life. Transportation of a patient to an emergency hospital seems quite simple but in actuality, it is quite difficult and gets more difficult during peak hours. In our Ambulance Booking System, people can easily book an ambulance. There are three major modules namely User, Ambulance, and Hospital. Users can register and log in using credentials. Users can edit their profile and change their password in an emergency. Any Upcoming Ambulance Booking details if anyone wants to Book an Ambulance or if there is an Emergency. For booking an ambulance users have to select ambulance size, pick-up point & hospital, and date & time. In an emergency will automatically book the nearest ambulance & hospital. Users will get a list of All the bookings of Ambulances. The front-end involves Html, CSS, and

1. IMAGE SEGMENTATION AND OBJECT
DETECTION

2. INTRODUCTION
 Image segmentation and object detection are crucial techniques in computer
vision that enable computers to understand visual data. By automatically
identifying and segmenting objects in images and videos, these techniques
have immense applications in autonomous vehicles, medical imaging, and
security systems. They allow us to extract valuable information, make informed
decisions, and take action based on visual data. From detecting abnormalities
in medical scans to identifying hazards on the road or tracking individuals in
crowded spaces, these technologies have the potential to transform how we
interact with our surroundings.

3. What is Image Segmentation?
 Image segmentation is the process of dividing an image into multiple
segments or regions based on certain characteristics such as color, texture, or
shape. This technique is an essential part of computer vision as it allows for
the identification and analysis of individual objects within an image.

4. Types of Image Segmentation
 Thresholding is a simple yet effective technique for image segmentation. It
involves selecting a threshold value and assigning all pixels above or below that
value to a certain class. This method is fast and easy to implement, but it may not
work well with images that have varying lighting conditions or complex
backgrounds.
 Clustering is another popular technique for image segmentation. It involves
grouping similar pixels together based on their color, texture, or other features.
This method can handle more complex images than thresholding, but it may be
slower and require more computational resources.
 Edge detection is a technique that involves detecting edges in an image and using
them as boundaries for segmentation. This method can be very accurate, but it
may also produce noisy results and require post-processing.
 Region growing is a technique that involves starting with a seed pixel or region and
gradually expanding it by adding neighboring pixels that meet certain criteria. This
method can produce very precise segmentations, but it may also be sensitive to
initial conditions and require manual tuning.

5. What is Object Detection?
 Object detection is a fundamental task in computer vision that involves
identifying the presence and location of objects within an image or video
stream. It plays a critical role in many real-world applications such as
autonomous vehicles, surveillance systems, and medical imaging.
 The importance of object detection lies in its ability to enable machines to
interpret visual data and make decisions based on that information. For
example, an autonomous vehicle must be able to detect and track other
vehicles, pedestrians, and obstacles in its environment in order to navigate
safely and efficiently.

6. Object Detection Techniques
 Haar cascades are a popular object detection technique that uses features
like edges, lines, and corners to detect objects. They are fast and efficient,
but can be sensitive to changes in lighting and background.
 HOG, or Histogram of Oriented Gradients, is another popular technique that
uses gradients to detect edges and shapes. It is more robust to changes in
lighting and background, but can be slower than Haar cascades.
 Deep learning-based methods, such as Convolutional Neural Networks (CNNs),
have become increasingly popular for object detection due to their ability to
learn complex features from large datasets. They are highly accurate and can
handle a wide range of objects and backgrounds, but can be computationally
intensive and require large amounts of training data.

7. Applications of Image Segmentation and
Object Detection
 Autonomous Vehicles: Image segmentation and object detection play a critical role in self-
driving cars. They help in identifying and tracking objects like pedestrians, vehicles, traffic
signs, and road boundaries, enabling the vehicle to make informed decisions and navigate
safely.
 Medical Imaging: In the medical field, image segmentation is used for identifying and
delineating specific structures or regions of interest in medical images, such as MRI, CT scans,
and X-rays. Object detection can be used to find abnormalities or anomalies in these images.
 Robotics: Image segmentation and object detection are essential for robotic systems to
interact with the environment effectively. Robots can identify and grasp objects, navigate
obstacles, and perform tasks by understanding the scene around them.
 Gaming: Image segmentation and object detection can be used to create interactive and
immersive gaming experiences, where the game can interact with the player's environment.
 Artificial Intelligence in Photography: Image segmentation can be used for background
removal, enabling users to easily change backgrounds or create artistic effects in photos.

8. Current Work done(CCTV number plate
detection)
 Users can upload an image of a vehicle's number plate, which is then
processed using OpenCV. The first step is to determine the diameter of circular-
shaped objects in the image. After logging in, users can select the 'Number
Plate' option to check if the number plate belongs to a stolen vehicle. A Haar
Cascade classifier is used to detect the numbers on the number plate, utilizing
object identification. Positive and negative images are used to train the
algorithm. Overall, a pre-trained Haar Cascade number plate recognizer is
employed to identify number plates in the given image.

9. Continuation
 This project focuses on enhancing communication and security in CCTV
systems by incorporating IoT features and alerts. By utilizing AI and image
processing, the aim is to improve accuracy in detecting criminals and
promptly notifying users of security breaches. The application targets small
and medium-sized businesses, providing them with responsive surveillance
and better communication capabilities with their CCTV systems. While some
features like live CCTV face mask detection and live footage of car number
detection couldn't be implemented due to GPU limitations, future
collaborations with better systems are sought for their implementation.

10. Improvements on the previous work
 Further improvements can be done using techniques like Convo LSTM + CNN to
reach even higher accuracies, such as 98% or 99%.
 Usage of newer technologies as well as hardware can help achieve better
accuracy and be more time and space efficient.