Discover mohamedameen93/Lane-lines-detection-using-Python-and-OpenCV

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Created about 7 years ago
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mohamedameen93

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In this project, I used Python and OpenCV to detect lane lines on the road. I developed a processing pipeline that works on a series of individual images, and applied the result to a video stream.

CS-7641-Machine-Learning-Notes

In this repository, I will publish my notes for GaTech's Machine Learning course CS7641.

200

CS-6210-Advanced-Operating-Systems-Notes

In this repository, I will publish my notes for GaTech's Advanced Operating Systems course (CS6210).

PostScript

German-Traffic-Sign-Classification-Using-TensorFlow

In this project, I used Python and TensorFlow to classify traffic signs. Dataset used: German Traffic Sign Dataset. This dataset has more than 50,000 images of 43 classes. I was able to reach a +99% validation accuracy, and a 97.3% testing accuracy.

HTML

CS-7642-Reinforcement-Learning-Notes

In this repository, I will publish my notes for GaTech's Reinforcement Learning course CS7642.

Advanced-Lane-Finding-Using-OpenCV

In this project, I used OpenCV to write a software pipeline to identify the lane boundaries in a video from a front-facing camera on a car.

Jupyter Notebook

An-Autonomous-Vehicle-System-For-Carla

In this project, we built ROS nodes to implement the core functionality of the autonomous vehicle system, including traffic light detection and classification, vehicle control control, and waypoint following.

Jupyter Notebook

Behavioral-Cloning-End-to-End-Learning-for-Self-Driving-Cars

In this project, I used a deep neural network (built with Keras) to clone car driving behavior. The dataset used to train the network is generated from Udacity's Self-Driving Car Simulator, and it consists of images taken from three different camera angles (Center - Left - Right), in addition to the steering angle, throttle, brake, and speed during each frame. The network is based on NVIDIA's paper End to End Learning for Self-Driving Cars, which has been proven to work in this problem domain.

Jupyter Notebook

Vehicle-Path-Planning-Algorithm

In this project, our goal is to design a path planning algorithm that is able to a car around a simulated highway scenario, including traffic and given waypoints, telemetry, and sensor fusion data.

C++

Vehicle-Steering-Using-PID-Control

In this project, we implement a PID controller to steer the self driving car around the track in Udacity's Simulator.

C++

Semantic-Segmentation-using-Fully-Convolutional-Networks

In this project, we'll construct a fully convolutional neural network based on the VGGNet-16 architecture to perform semantic segmentation on a video captured from a front facing camera mounted on a vehicle dashboard to identify the drivable surface area.

Python

Vehicle-Detection-and-Tracking

In this project, I built a software pipeline to detect vehicles in a video.

Jupyter Notebook

Vehicle-Steering-Using-Model-Predictive-Control

The main goal of the project is to implement in C++ Model Predictive Control to drive the vehicle around the simulator track.

C++

Vehicle-Localization-Using-Particle-Filters

In this project, we will implement a 2-dimensional particle filter in C++. The particle filter will be given a map and some initial localization information (analogous to what a GPS would provide) to localize a vehicle.

C++

kaggle_titanic

Predicting Titanic survival probability using several classification models

Python

coursera-ml

Coursera Machine Learning Assignments

MATLAB

mohamedameen93/Lane-lines-detection-using-Python-and-OpenCV

mohamedameen93

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