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Devised an algorithm written to detect lane and estimate road curvature from live footage of an Autonomous Vehicle with added intelligent lane prediction in case lane is not detected <Completed>#

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Histogram Equalization and Lane Detection

This Repository consists of two Projects

Histogram Equalization

Introduction

Histogram is a graphical representation of the intensity distribution of an image. In simple terms, it represents the number of pixels for each intensity value considered. Sometimes, images are taken in very dark or poor lightning conditions. An example of poor exposure image is given below in Fig 1

Poor Exposure ImagePoor Exposure Image

Histogram equalisation helps to enhance the intensity level of pixels with poor exposure.It also used to enhance the contrast in the image. The Histogram for above image is shown in Fig 2 and 3

Histogram ImageImage after Histogram equalization

Adaptive Histogram ImageImage after Adaptive Histogram equalization

Lane Detection

Bird Eye View

A bird’s-eye view is an elevated view of an object from above, with a perspective as though the observer were a bird. For this I used a constant size "Parking frame" as shown in the Fig. 4 as target area.

Birds Eye ViewBuilding Frame for Birds Eye View

Then Homography was performed using corners of the above frame and a portrait rectangle.

Birds Eye Sliding Window

Birds Eye | Sliding Window

Back Propogation

Back projection was done using inverse of previously computed homography matrix H.

Results

Results Lane

Running Instructions

git clone git@github.com:nvnmangla/Lane_detection.git
cd Lane_detection/

To run Histogram

python3 solution.py --isHist=True

Else

python3 solution.py

About

Devised an algorithm written to detect lane and estimate road curvature from live footage of an Autonomous Vehicle with added intelligent lane prediction in case lane is not detected <Completed>#

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