This ppt is based on counting the number of vehicle on traffic roads, highways,etc. This project is based on python.

1. A END SEMESTER MINOR PROJECT PRESENTATION
ON
VEHICLE COUNTING FOR TRAFFIC MANAGEMENT
PRESENTED BY
ADEEBA NADEEM
DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING

2. CONTENT
 INTRODUCTION
 PROBLEM STATEMENT
 VEHICLE COUNTING SYSTEM
 COMPONENTS OF VEHICLE COUNTING
 IMPLEMENTATION/SCREENSHOTS
 CONCLUSION
 REFERENCES

3. INTRODUCTION
 The Increased demand for smart cities, from both developed and developing
nations, necessitates the deployment of digital techniques to analyze the road
traffic density, especially in mega cities.
 Traffic Management plays an important role in city planning and regulating the
density of vehicles on the road.
 Main Objective is to detect and count the number of cars is to be able to do so on
roads, highways and in small lanes etc.
 Classification and Counting of Vehicles, both moving and stationary, are done by
applying image processing (video content analysis) algorithms on video streams
taken from a stationary camera.
The vehicle counting project is mainly used -
1. For Traffic management and planning.
2. Congestion Control.
3. Parking management.

4. PROBLEM STATEMENT
The main Objective for developing this system is to collect vehicle count and classification
data and also track and count the detected vehicle when they leave the frames or makes use
of a counting line drawn across a road.

5. VEHICLE COUNTING SYSTEM
INPUT
FRAMES
OBJECT
DETECTIO
N
SELECTION
OF REGION
OF
INTEREST
VEHICLE
TRACKING
VEHICLE
COUNTING

6. VEHICLE COUNTING SYSTEM CONT.
1. The System uses an existing video sequence. The Recorded Video data or real time
video data is required as input. Then it is divided into frames.
2. Haar Cascade Classifier is a method utilized for detecting object. In obtaining object
detection value, Haar-like feature value was calculated using integral image.
3. Region of interest is a specific region that is extracted from the given frame.
Background subtraction performs a subtraction between current frame and the
background model to determine the foreground mask. Next, thresholding and image
dilation are applied to the output of Background subtraction.
4. Vehicle tracking is composed of identifying the detected vehicle continuously in a video
sequence. The system detects each moving vehicle and the detected vehicle is
surrounded with a rectangle. The size of the rectangle refers to the area of the detected
vehicle.
5. Every passing vehicle object inside ROI was tracked based on its position. Count line
has been introduced in the system. When detected vehicles pass over the count line will
only be counted otherwise not.

7. VEHICLE COUNTING SYSTEM CONT.
 Vehicle counting is carried out using the virtual line method. This virtual line acts as a
counter from which the count is updated. For each vehicle that enters into the frame and
crosses the virtual line, the count is incremented. While counting vehicles, it’s very
important to count each vehicle only once.
HOG-SVM-based Vehicle Classification:
 Support Vector Machine is a supervised machine learning algorithm, which is used for
image classification and pattern recognition. An SVM model can be considered as a
point space wherein multiple classes are isolated using hyperplanes. (Support vector
machine is basically a hyperplane which separates and classify multiple classes very
well). The SVM algorithm is widely used for object-based classification.
 Histogram of oriented gradients is a feature descriptor used in image processing for
object detection through their shapes.

8. COMPONENTS OF VEHICLE
COUNTING SYSTEM
VEHICLE COUNTING SYSTEM MADE UP OF THREE COMPONENTS-
a) Detector
b) Tracker
c) Counter
 The Detector identifies vehicles in a given frame of video and returns a list of
bounding boxes around the vehicle to the tracker.
 The Tracker uses the bounding boxes to track the vehicle in a subsequent frames.
It is also used to update the tracker periodically.
 The Counter counts the vehicle when they leave the frames or makes use of a
counting line drawn across a road.

9. IMPLEMENTATION

10. ACQUIRE THE DATASET
(INPUT VIDEO)

12. PREPROCESSING

13. OBTAINING FRAME FROM VIDEO

14. GAUSSIAN BLUR

15. BACKGROUND
SUBTRACTION

16. THRESHOLDING

17. DILATION

18. OUTPUT
VEHICLE DETECTION AND
COUNTING

19. VEHICLE COUNTING
OUTPUT

20. CONCLUSION
The vehicle traffic data from this application can be used to count and classify
vehicles on busy routes. Once this application is used to gather the data of vehicle
types. Open Source Computer Vision Library (OpenCV) and Python Programming
language is used to implement the method developed. The system in this is to
calculate the number of vehicles passing on the road. It was based on the detection of
vehicles that cross a virtual line.

21. REFERENCES
■ Ashaashvini A/P Mutharpavalar,Measuring Of Real-Time Traffic Flow Using
Video From Multiple IP-Based Cameras,2019, IEEE International
Conference on Signal and Image Processing Applications.
■ Amit Ghosh, An Adaptive Video-based Vehicle Detection, Classification,
Counting, and Speed-measurement System for Real-time Traffic Data
Collection,2019.
■ Mirthubashini J ,Video based vehicle counting using Deep Learning
Algorithm,2020 6th International Conference on Advanced Computing &
Communication Systems (ICACCS).
■ Mr. Nikhil Chhadikar,Image processing based Tracking and Counting
Vehicles, Third International Conference on Electronics Communication and
Aerospace Technology [ICECA 2019].