mini project on the topic parking space counter using opencv

1. PARKING SPACE COUNTER
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
COLLEGE OF ENGINEERING MUTTATHARA

2. OUR TEAM
1. ABHAY A V
2. ADITHYAN P B
3. KRISHNARAJ R
4. AKSHAY S NAIR
UNDER THE GUIDANCE OF
MRS SUJAKUMARI N R

3. INTRODUCTION
 The parking space counter project employs Python and OpenCV to create
an automated system for accurately counting available parking spots.
 By analyzing video or images, the system utilizes image processing and
object detection techniques to identify vehicles and segment parking
spaces.
 This real-time information on parking availability assists drivers in
efficiently locating open spots, reducing frustration and saving time in
crowded urban areas or busy parking lots.

4. PURPOSE
 The purpose of the project is to develop a parking space counter using Python and OpenCV.
 It aims to accurately detect and count the number of available parking spaces in real-time.
 This system can help efficiently manage parking lots and provide users with up-to-date
information on parking availability.

5. SCOPE
 Efficient parking management through real-time counting of available spaces.
 Enhanced user experience by providing up-to-date parking availability information
 Optimize resource allocation and improve revenue by analyzing parking occupancy data for
better planning and utilization of parking infrastructure.

6. ABSTRACT
 This project aims to create a system that counts available parking spaces using Python and
OpenCV.
 By analyzing video feeds, the system accurately tracks and detects vehicles in real-time,
providing up-to-date information on parking availability.
 The project intends to improve parking management, enhance user experience, and optimize
resource allocation for efficient use of parking spaces..

7. Problem Statement
 Inefficient utilization: Parking lots lack accurate information on availability, resulting in
wasted resources and frustrated drivers.
 Manual counting errors: Human operators often make mistakes in counting parking
spaces, leading to inaccurate information and ineffective management.
 Limitations of sensor-based systems: Expensive and maintenance-intensive sensor
installations can be unreliable and require extensive infrastructure modifications.

8. PROPOSED SYSTEM HIGHLIGHTS
Real-time and accurate parking space detection, leveraging advanced computer vision
algorithms and real-time video analysis to provide timely and precise identification of available
and occupied parking spaces.
High-precision occupancy counting, harnessing the power of Python and OpenCV to ensure
accurate counting of parking spaces, enabling users to efficiently locate vacant spots and
optimize their parking decisions.
User-friendly interface designed to deliver a seamless parking experience, presenting the
parking space occupancy information in an intuitive and visually appealing manner, simplifying
the process of finding available parking spaces for both drivers and parking lot operators.

9. EXISTING SYSTEMS
 Ultrasonic Sensor-based Systems: Utilizing ultrasonic sensors installed in parking spaces, these
systems detect vehicle presence by measuring the distance to the nearest object. The sensors
determine if a parking space is occupied or vacant based on the detected distances.
 Magnetic Loop Sensor Systems: Embedded in the ground of parking spaces, magnetic loop
sensors detect changes in the electromagnetic field caused by vehicles. By sensing these
disruptions, they accurately determine the occupancy status of parking spaces.
 Infrared Sensor-based Systems: Infrared sensors are placed at entrance and exit points of
parking spaces. They emit infrared beams and detect interruptions in the beams caused by
vehicles. This allows the system to determine whether a parking space is available or occupied
based on the detected interruptions.

10. PROCESS SPECIFICATION
 Video feed acquisition
 Vehicle detection
 Vehicle tracking
 Occupancy analysis
 Space counting
 Real-time processing
 Accuracy
 User interface
 Integration
 Performance optimization

11. LITERATURE
SURVEY

12. TITLE METHOD
Conventional
Automation Techniques
[1]
 The conventional automation techniques used in the
project topic of "parking space counter using Python
and OpenCV" involve several key steps.
 The process begins with image acquisition, where
images or video frames of the parking slot are
captured using cameras or imaging devices.
 These acquired images undergo preprocessing
techniques such as resizing, noise reduction etc.
 Object detection or segmentation algorithms are then
applied to identify parking spaces or vehicles.
 Finally, a counting algorithm analyzes the tracked
objects' trajectories to determine the occupancy
status of each parking space.

13. TITLE METHOD
Detect and Segment
Parking Spaces[2]
 Utilize edge detection techniques such as Canny
edge detection to extract the boundaries of parking
spaces from the input image or video feed.
 Apply contour analysis algorithms to detect and
isolate individual parking spaces based on the
extracted edges, considering factors like size, shape,
and connectivity.
 Implement region-based segmentation approaches
like GrabCut or watershed segmentation to refine the
detected parking spaces and generate accurate
segmentation masks for occupancy analysis and
counting.

14. TITLE METHOD
Count and Display Result
[3]
 Counting: Utilize the detection and segmentation
techniques to identify occupied and vacant parking
spaces. Keep a count of the vacant spaces as vehicles
enter and exit the parking area.
 Result Display: Develop a user-friendly interface
that presents the parking space count in real-time.
 Real-time Updates: Implement a mechanism to
continuously update the displayed result as vehicles
occupy or vacate parking spaces. This ensures that
the parking space count is always up to date and
provides accurate information

15. TITLE METHOD
Performance Evaluation
[4]
 Accuracy Assessment: The system's accuracy in
detecting and counting parking spaces will be
evaluated by comparing the results with ground
truth data obtained through manual inspection
or existing parking management systems.
 Real-Time Processing: The system's
performance in processing video feeds and
providing real-time occupancy updates will be
measured in terms of latency, ensuring that the
information is delivered promptly to users.
 Robustness and Scalability: The system will be
tested for robustness against various lighting
conditions, vehicle types, and parking lot
configurations.

16. TITLE METHOD
Computer Vision-Based
Techniques [5]
 Object Detection: Computer vision algorithms are
employed to detect vehicles within the parking lot by
analyzing video feeds. This involves techniques like
Haar cascades, HOG (Histogram of Oriented
Gradients), or deep learning-based
 Image Segmentation: Image segmentation
algorithms, such as contour detection or semantic
segmentation, are utilized to segment the parking
spaces from the video frames, distinguishing
between occupied and vacant areas.
 Tracking and Counting: Vehicle tracking algorithms
are implemented to track the movement of vehicles
within the parking lot over time.

17. MODULE DESCRIPTION
 Video Feed Acquisition Module: This module is responsible for capturing real-time video
feeds from cameras placed in the parking area. It establishes the connection with the
cameras, receives the video stream, and provides the frames for further processing.
 Vehicle Detection and Tracking Module: This module employs computer vision algorithms
and techniques to detect and track vehicles within the video frames. It utilizes object
detection models and algorithms to identify vehicles accurately and track their movement
across frames.

18.  Occupancy Analysis and Counting Module: This module analyzes the detected vehicles and
performs occupancy counting. It processes the tracked vehicles, determines their occupancy status
status (occupied or vacant), and updates the count of available parking spaces in real-time.
 User Interface Module: This module is responsible for presenting the occupancy information to
the users through a user-friendly interface. It displays the parking space count, highlights the
available and occupied spaces, and provides visual feedback to aid users in finding vacant parking
spots.
 Integration Module: The integration module combines the functionalities of the above modules,
ensuring seamless coordination and communication between them. It establishes the flow of data
and information, enabling the overall system to work cohesively in detecting, tracking, and
counting parking spaces.

19. DIAGRAMS

20. FLOW DIAGRAM

21. USE-CASE DIAGRAM

22. CLASS DIAGRAM

23. SEQUENTIAL DIAGRAM

24. ACTIVITY DIAGRAM

25. ER DIAGRAM

26. REQUIREMENT
ANALYSIS

27. HARDWARE REQUIREMENTS
 Camera: Any camera compatible with your computer.
 Computer:
 Processor: Intel Core i5 or higher.
 Memory: Minimum 8GB RAM.
 Storage: Sufficient free disk space to store images and the application.
 Display: Minimum resolution of 1280x720 pixels.
 Graphics Card: Recommended for improved performance (e.g., NVIDIA GeForce or AMD
Radeon).
 Operating System: Windows 10, macOS, or Linux.
 Power: Reliable power supply to ensure uninterrupted operation.
 Internet: Optional for accessing online resources or remote monitoring.

28. SOFTWARE REQUIREMENTS
 Python
 OpenCV
 IDE (e.g., PyCharm, VS Code)
 Operating System (Windows, macOS, Linux)
 Python Packages (numpy, matplotlib, etc.)
 Version Control System (Git)
 Image Processing Libraries (Pillow, scikit-image)
 Web Framework (Flask, Django)
 Database (MySQL, PostgreSQL, SQLite)
 Documentation Tools (Jupyter Notebook, Markdown)
 Collaboration Tools (GitHub, GitLab)

29. FUNCTIONAL REQUIREMENTS
 Capture live video feed from a camera.
 Process the video frames using OpenCV for object detection.
 Detect and track parking spaces within the video frames.
 Count the number of available and occupied parking spaces.
 Display the parking space count in real-time.
 Generate alerts or notifications when parking spaces are occupied or become available.
 Provide an option to adjust sensitivity or parameters for object detection.

30. NON FUNCTIONAL REQUIREMENTS
 Performance
 Reliability
 Scalability
 Accuracy
 Efficiency
 Usability
 Maintainability
 Compatibility
 Security
 Portability

31. IMPLEMENTATION

33. RESULT

38. WHAT WE LEARNT
 Proficiency in Python programming and OpenCV library for image processing and analysis.
 Understanding of computer vision techniques for object detection and tracking.
 Ability to design and implement an automated system for counting and monitoring parking
space occupancy, enhancing skills in image processing, and real-time analysis.

39. FUTURE SCOPE
 Integration with IoT: The project can be expanded to incorporate IoT technologies to enable
real-time monitoring and management of parking spaces, including features like occupancy
alerts and reservation systems.
 Advanced Analytics: By leveraging machine learning algorithms and advanced analytics
techniques, the project can provide insights into parking patterns, trends, and optimization
strategies for efficient space utilization.
 Mobile Application Development: Creating a mobile application that interfaces with the parking
space counter system would allow users to check real-time parking availability, receive
notifications, and navigate to available spaces..

40. CONCLUSION
 In conclusion, the project topic "Parking Space Counter using Python and OpenCV"
demonstrates the power of computer vision and automation in efficiently managing parking
spaces. By combining Python programming and OpenCV, the project achieves real-time
occupancy counting and monitoring. With further advancements in IoT integration and data
analytics, it has the potential to revolutionize parking systems and enhance urban mobility

41. REFERENCES
 1. Reference:
Authors: A. Amin, H. M. Mohd, A. M. Daud, M. A. Mahfuz, and S. S. R. Abu-Bakar
Title: Automatic Parking Space Detection System Using OpenCV
Published in: 2017 6th International Conference on Electrical Engineering and Informatics
(ICEEI)
Link: [IEEE Xplore](https://ieeexplore.ieee.org/abstract/document/8293170)
 2. Reference:
Authors: P. Subbaraj and D. K. Kumar
Title: Vehicle Parking Slot Detection Using Image Processing
Published in: 2016 International Conference on Computer Communication and Informatics
(ICCCI)
Link: [IEEE Xplore](https://ieeexplore.ieee.org/abstract/document/7439502)