This document presents a traffic jam detection system using image processing. The system uses cameras to capture video frames of traffic at regular intervals. The frames are analyzed using image processing techniques like grayscale conversion, erosion, and dilation to detect vehicles and motion. An android application is also developed to provide users with real-time traffic density information for different locations based on the image analysis. The proposed system aims to provide a low-cost and reliable alternative to existing magnetic and infrared-based traffic detection methods.

1. TRAFFIC JAM DETECTION USING IMAGE PROCESSING
PRESENTED BY
D.M.V.S.SAI
10F81A0586
CSE DEPARTMENT

2. ABSTRACT:
1. To control traffic management image processing has been
introduced
2. Easy to calculate traffic density which is cost-effective
3. Image processing can detect vehicles in any climatic
conditions
4. Using the information given by image processing technique, an
android application is developed, which the user will get traffic
density at location of his choice

3. CONTENTS:
• Introduction
• Existing methods
• What is image processing?
• procedure
• Proposed system
• conclusion

4. INTRODUCTION:
• Works with latest technologies like digital image processing
• System consists of cameras that monitors traffic by capturing
videos
• Extracts video frames at regular intervals and frames are
compared to determine whether there is traffic jam or not
• Android application which was developed will give the list of
locations from database having density of traffic

5. EXISTING METHODS:
• Magnetic loop detectors are used to count number of vehicles
using magnetic properties
• Inductive loop detectors provide cost effective solution
• Light beams like IR,LASER are used
DRAWBACKS:
• These detectors need separate system for traffic detection &
surviallance
• Detectors failure rate is more in poor road surfaces
• Fails in different climatic conditions

6. WHAT IS IMAGE PROCESING?
• Image processing is the process of taking captured images as
binary data as primary input
• The captured digital images are processed that consists of elements
of location & value called as picture elements
• Operations of image processing are
sharpening,blurring,brightening

7. PROCEDURE:
• KEYPOINTS ARE:
1.
2.
3.
4.
5.
Image analysis
Object detection
Typed object count
Motion detection
Result representation

8. PHASES OF IMAGE PROCESSING:
PHASE I:
• Videos frames extracted are converted into gray scale
• Any color that converts to grayscale must obtain values from
red,blue,green(RGB) colors
• The greyscale image is then converted to binary
• RGB to greyscale conversion is as follows:

9. PHASE II:
 Two operations used here:
1. Erosion
2. Dilation
 EROSION:
• It decreases the size of objects & removes disturbances in
the image

10.  DILATION:
• It increases the size of objects by filling the holes & broken
areas in the image by connecting them

11. PHASE III:
 Two operations used here:
1. Motion detection
2. Vehicle detection
 Motion detection:
• Here two consecutive frames are taken & their histograms
are compared with their threshold value
• The motion of the image is detected by selecting an
appropriate threshold value
 Vehicle detection:
• The profile of the roads is divided into sub-profiles
• The length of the sub-profiles should be equivalent to length
of the vehicle

12. PROPOSED SYSTEM:
Architecture consists of 5 components:
1. Traffic management
2. Roadway system
3. Server
4. Android application
5. Camera

13. ARCHITECTURE:

14. ADVANTAGES:
 This technique is cost-effective , reliable & flexible
Free flow of traffic
Gives an opportunity for the user to reach the destination in
less time
They provide more traffic information,combine both
surveillance and traffic control technologies

15. CONCLUSION:
 Image processing is a better technique to control traffic jam
 It is more consistent in detecting vehicles presence as it
visualizes actual traffic frames
 Overall the system is good, but it still needs improvement to
achieve hundred percent accuracy

16. REFERENCES:
• Zehang sun, george bebis, and Ronald Miller, “ on-road
vehicle detection using evolutionary gabor filter
optimization” Mar-Apr 2012
• Khan muhammed nafee Mostafa, qudrat-E-alahy Ratul,
“Traffic jam detection system”,pp 1-4
• Traffic safety facts, US Department of Transport, december
2012,pp 1-2