The document discusses a real-time traffic sign analysis system guided by MS. P.P. Lokhande, focusing on detection and recognition techniques such as color segmentation, edge detection, and shape-based detection. It highlights the importance of this system in advanced driver assistance systems (ADAS) to prevent accidents caused by human error, while addressing challenges like illumination, occlusion, and weather conditions. The conclusion emphasizes that improving detection and recognition can enhance the effectiveness of traffic sign recognition systems.

1. REAL TIME TRAFFIC
SIGN ANALYSIS
Presented By-
Rakesh Ravaso Patil
T CO ‘B’ 12276
Guided By-
Ms. P. P. Lokhande

2. Overview
 Introduction
 Traffic sign analysis
 Color segmentation
 Edge detection
 Shape based detection
 Recognition
 Binary Thresholding
 Recognition and Matching using IPP
 Recognition of traffic signs using FPGA hardware
 How TSR is works?
 Conclusion

3. INTRODUCTION
 Advanced Driver Assistance Systems (ADAS)
 Lane Departure Warning
 Night Vision
 Automatic Parking
 Blind Spot Detection
 Traffic Sign Recognition
 First used In
 BMW 7 Series
 Volkswagen Phaeton

4. WHY WE REQUIRE THIS?
 Sleepy driver crashes SUV on Mumbai-Pune
Expressway, 7 passengers killed. (TOI, March 5)
 Human error behind most Expressway mishaps.
(TOI, March 5)
 In 2012, the expressway, witnessed 475 accidents in
which 105 people died.
 MSRDC plan:
 Trauma Care & Copter Service
 CCTV Cameras
 Truck Terminals
 Reducing U-Turns

5. TRAFFIC SIGN
Possible
Sign Type Sign Shape
(Border) Colors
Triangle,
Restricting &
Red, Blue, Black Rectangle, Octagon,
Warning
Circle
Information Blue, Red Rectangle
Highway
Green Rectangle
Information
Table: Standard Traffic Sign

6. REAL TIME TRAFFIC SIGN ANALYSIS
 Detection
 Recognition
 Problem facing
 Illumination affects the color analysis.
 Occlusion affects the shape analysis.
 Weather conditions such as rain, snow or fog
affect the shape extraction.
 Physically damaged or changed surface metal of
traffic signs affects the recognition.

7. TRAFFIC SIGN ANALYSIS
Fig: Steps of TSR System

8. COLOR SEGMENTATION
Fig: Traffic sign and Red/Blue segmented image

9. COLOR SEGMENTATION-ADVANTAGES
 Eliminates undesired colors, thus the number of
edge pixels in the edge detection process decreases.
 The complexity decreases since only edge pixels are
processed.
 Fault detections decrease in the detection process.
 Color segmentation gives information about the
border color and the inner color of the sign.

10. EDGE DETECTION
 Identifying points in a digital image at which the
image brightness changes sharply
Fig: Edge image with color segmentation

11. SHAPE BASED DETECTION
 Types: Triangle, Circle and Rectangle
 TRIANGULAR SIGN DETECTION
 Hough Transform using Slope-Intercept Line equ.
y=a.x + b
where: x,y are coordinates
a is the slope of the line
b is the constant parameter…
 Use of Polar Coordinates instead of Cartesian
Coordinates.

12. TRIANGULAR SIGN DETECTION
x.cosΘ + y.sinΘ=r
Where: r is distance between line & Origin
Θ is angle from origin to the closest point to line

13. TRIANGULAR SIGN DETECTION
Fig: Edge Image of a Triangular Fig: Detected Lines after applying
Traffic sign Hough Transform

14. CIRCULAR SIGN DETECTION
 Circular Hough Transform using parametric equation
of Circle:
(x-xc)² + (y-yc)² = r²
 Because of Perspective distortion Circular traffic
sign may appear as elliptical.
(x-xc)² + k.(y-yc)² = r²

15. CIRCULAR SIGN DETECTION
Fig: Detected Circle after applying CHT Fig: Detected Ellipse after applying
Ellipse Detection

16. RECTANGULAR SIGN DETECTION
Fig: Detected Lines of Rectangular
Traffic Sign

17. RECOGNITION
 A binary image is generated using ROI of the image.
 Morphological operations are applied to the binary
image in order to remove the unwanted pixels.
 Informative Pixel Percentage (IPP).

18. BINARY THRESHOLDING
 ROI is the informative part of the image.
 Traffic sign consists of only two different colors. One
is the informative color of ROI and the other is the
background color.
Fig: Output of Binarization Process

19. RECOGNITION AND MATCHING
USING IPP
 TRIANGULAR SIGN RECOGNITION
Fig: Divided Regions of Triangular Sign

20. CIRCULAR, RECTANGULAR SIGN
RECOGNITION
Fig: Divided Regions of Circular and Rectangular Sign

21. RECOGNITION OF TRAFFIC SIGNS
USING FPGA HARDWARE
 VIRTEX5-FX70T FPGA
 XILINX Platform flash PROM
 DDR2 SDRAM
 LCD Display

22. HOW TSR IS WORKS?

23. CONCLUSION
 Automatic traffic sign detection and recognition is an
important part of an ADAS.
 Traffic symbols have several distinguishing features
that may be used for their recognition and
detection.
 There are several factors that can hinder effective
detection and recognition of traffic signs.
 The performance of the TSR system can be improved
with increasing the number of divided regions.