hog-features

Vehicle detection using machine learning and computer vision techniques for Udacity's Self-Driving Car Engineer Nanodegree.

Computer Vision and Image Recognition algorithms for R users

Convolutional Autoencoder for Loop Closure

Training SVM classifier to recognize people expressions (emotions) on Fer2013 dataset

Vehicle detection, tracking and counting by SVM is trained with HOG features using OpenCV on c++.

A vehicle detection and tracking pipeline with OpenCV, histogram of oriented gradients (HOG), and support vector machines (SVM).

HOG feature extractor with simple python implementation

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.

Detecting Cars in real time and identifying the speed of cars and tracking

Detects Pedestrians in images using HOG as a feature extractor and SVM for classification

Image processing Toolkit in R

MATLAB implementation of a basic HOG + SVM pedestrian detector.

SVM using HOG descriptors implemented in fragment shaders

Histogram Of Oriented Gradients

Android application which uses feature extraction algorithms and machine learning (SVM) to recognise and translate static sign language gestures.

Python module for face recognition with OpenCV and Deep Learning.

Term 1, Project 5 - Udacity Self Driving Car Nanodegree

Detecting vehicles using HOG features and SVM

Vehicle detection and tracking using linear SVM classifier

vehicle detection by HOG and color features

Compare different HOG descriptor parameters and machine learning algorithms for Image (MNIST) classification

Several methods for detecting pedestrians either in images or in camera feed, using OpenCV and Python. With inspiration and code from Adrian Rosebrock's PyImageSearch blog.

Created a vehicle detection and tracking pipeline with OpenCV, histogram of oriented gradients (HOG), and support vector machines (SVM). Optimized and evaluated the model on video data from a automotive camera taken during highway driving.

Recognize traffic sign using Histogram of Oriented Gradients (HOG) and Colorspace based features. Support Vector Machines (SVM) is used for classifying images.

Detect, recognize and verify faces using hybrid features: “deep” features from VGG-net + HoG + LBP. Hybrid Features help increase recognition significantly

HOG implementation for pedestrian detection.

This research uses computer vision and machine learning for implementing a fixed-wing-uav detection technique for vision based net landing on moving ships. A rudimentary technique using SIFT descriptors, Bag-of-words and SVM classification was developed during the study.

Training AdaBoost Classifier of 500 estimators to classify HOG&LBP features of TB &NTB (Tuberculosis) chest X-ray images

Person Detection using HOG Feature and SVM Classifier