Traffic sign detection

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This document discusses traffic sign detection and recognition. It outlines the key steps in a traffic sign recognition (TSR) system, including color segmentation to isolate the sign, edge detection to find sign boundaries, and shape-based detection to identify sign type (triangle, circle, rectangle). Recognition involves binary thresholding of the region of interest and matching to known signs using informative pixel percentage. Challenges to TSR include varying illumination, occlusion, and weather conditions. The document also notes real-world applications of TSR in advanced driver assistance systems.

Engineering

TRAFFIC SIGN
DETECTION
Presented By-
Avijit Rai
Amit Jain
Guided By-
Ms. Amanpreet Kaur

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

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

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

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

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.

TRAFFIC SIGN ANALYSIS
Fig: Steps of TSR System

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

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.

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

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.

ARROW SIGN DETECTION
Fig: Detected Circle after applying CHT Fig: Detected Ellipse after applying
Ellipse Detection

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).

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

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

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.

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Rule 1 − Check for the blocks connected in series and simplify. Rule 2 − Check for the blocks connected in parallel and simplify. Rule 3 − Check for the blocks connected in feedback loop and simplify. Rule 4 − If there is difficulty with take-off point while simplifying, shift it towards right. Rule 5 − If there is difficulty with summing point while simplifying, shift it towards left. Rule 6 − Repeat the above steps till you get the simplified form, i.e., single block.

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Traffic sign detection

1. TRAFFIC SIGN DETECTION Presented By- Avijit Rai Amit Jain Guided By- Ms. Amanpreet Kaur

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 Sign Type Possible (Border) Colors Sign Shape Restricting & Warning Red, Blue, Black Triangle, Rectangle, Octagon, Circle Information Blue, Red Arrow Highway Information Green Arrow 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. ARROW SIGN DETECTION Fig: Detected Circle after applying CHT Fig: Detected Ellipse after applying Ellipse Detection

13. 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).

14. 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

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

16. 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.

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