Vehicle detection using machine learning and computer vision techniques for Udacity's Self-Driving Car Engineer Nanodegree.
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Updated
Oct 31, 2017 - Jupyter Notebook
Vehicle detection using machine learning and computer vision techniques for Udacity's Self-Driving Car Engineer Nanodegree.
Deep facial expressions recognition using Opencv and Tensorflow. Recognizing facial expressions from images or camera stream
Computer Vision and Image Recognition algorithms for R users
Training SVM classifier to recognize people expressions (emotions) on Fer2013 dataset
Convolutional Autoencoder for Loop Closure
Vehicle detection, tracking and counting by SVM is trained with HOG features using OpenCV on c++.
Face detection implementation with different methods and applications
A vehicle detection and tracking pipeline with OpenCV, histogram of oriented gradients (HOG), and support vector machines (SVM).
MATLAB implementation of a basic HOG + SVM pedestrian detector.
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, I…
Detects Pedestrians in images using HOG as a feature extractor and SVM for classification
Android application which uses feature extraction algorithms and machine learning (SVM) to recognise and translate static sign language gestures.
Detecting Cars in real time and identifying the speed of cars and tracking
Histogram Of Oriented Gradients
Term 1, Project 5 - Udacity Self Driving Car Nanodegree
Person Detection using HOG Feature and SVM Classifier
Traffic sign detection and classification
Image processing Toolkit in R
Detecting vehicles using HOG features and SVM
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