TRAFFIC SIGN DETECTION

Presented By-Avijit RaiAmit 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 TypePossible

(Border) ColorsSign Shape

Restricting &

WarningRed, Blue, Black

Triangle,

Rectangle,

Octagon, Circle

Information Blue, Red Arrow

Highway

InformationGreen 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 + bwhere: x,y are coordinates

a is the slope of the lineb 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.