Udacity Self-Driving Car Engineer Nanodegree projects.

Vehicle Detection by Haar Cascades with OpenCV

Vehicle Detection, Tracking and Counting

Created vehicle detection pipeline with two approaches: (1) deep neural networks (YOLO framework) and (2) support vector machines ( OpenCV + HOG).

Vehicle detection using YOLO in Keras runs at 21FPS

This is a Matlab lesson design for vehicle detection and recognition. Using cifar-10Net to training a RCNN, and finetune AlexNet to classify. Thanks to Cars Dataset:http://ai.stanford.edu/~jkrause/cars/car_dataset.html

OpenCV implementation of lane and vehicle tracking

Vehicle Detection with Convolutional Neural Network

real-time Vehicle Detection( tiny YOLO ver) and HOG+SVM method

Vehicle Detection, Tracking and Counting by Blob Detection with OpenCV

Vehicle Detection, Tracking and Counting by SVM with HOG Features with OpenCV

Detect vehicles in a video

This repository contains works on a computer vision software pipeline built on top of Python to identify Lanes and vehicles in a video. This project is not part of Udacity SDCND but is based on other free courses and challanges provided by Udacity. It uses Computer vision and Deep Learrning Techniques. Few pipelines have been tried on SeDriCa, IIT Bombay.

Detect and track vehicles in video

Perception algorithms for Self-driving car; Lane Line Finding, Vehicle Detection, Traffic Sign Classification algorithm.

This is one of the best vehicle recognition applications. It can determine the car's license plate number, color, model, brand and year.

Driving risk assessment with deep learning using a monocular camera. Related paper: https://arxiv.org/abs/1906.02859

The main objective of this project is to identify overspeed vehicles, using Deep Learning and Machine Learning Algorithms. After acquisition of series of images from the video, trucks are detected using Haar Cascade Classifier. The model for the classifier is trained using lots of positive and negative images to make an XML file. This is followed by tracking down the vehicles and estimating their speeds with the help of their respective locations, ppm (pixels per meter) and fps (frames per second). Now, the cropped images of the identified trucks are sent for License Plate detection. The CCA (Connected Component Analysis) assists in Number Plate detection and Characters Segmentation. The SVC model is trained using characters images (20X20) and to increase the accuracy, 4 cross fold validation (Machine Learning) is also done. This model aids in recognizing the segmented characters. After recognition, the calculated speed of the trucks is fed into an excel sheet along with their license plate numbers. These trucks are also assigned some IDs to generate a systematized database.

Vehicle detection and classification on a video from an Indian Highway.

According to YOLOv3 and SORT algorithms, counting multi-type vehicles. Implemented by Pytorch.

Graduation project repository, Real-time vehicle detection using two different approaches. HOG+SVM traditional approach and Deep Learning based approach using state of the art YOLO convolutional neural network.

Automatic detection and tracking of moving vehicles in a video from a surveillance camera

Term 1, Project 5 - Udacity Self Driving Car Nanodegree

An implementation of Faster R-CNN applied to vehicle detection.

A Network for detecting and classifying vehicle's front and rear

Vehicle detection implemented with you Only Look Once. It's an object detector that uses features learned by a deep convolutional neural network to detect an object.