There are 4 repositories under hog-features topic.
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
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).
Detects Pedestrians in images using HOG as a feature extractor and SVM for classification
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
Image processing Toolkit in R
SVM using HOG descriptors implemented in fragment shaders
Android application which uses feature extraction algorithms and machine learning (SVM) to recognise and translate static sign language gestures.
MATLAB implementation of a basic HOG + SVM pedestrian detector.
Histogram Of Oriented Gradients
Face detection implementation with different methods and applications
Person Detection using HOG Feature and SVM Classifier
Object detector from HOG + Linear SVM framework
Detecting Cars in real time and identifying the speed of cars and tracking
Term 1, Project 5 - Udacity Self Driving Car Nanodegree
Detecting vehicles using HOG features and SVM
Compare different HOG descriptor parameters and machine learning algorithms for Image (MNIST) classification
Attendance System using Face Recognition (HOG)
Vehicle detection and tracking using linear SVM classifier
Recognize traffic sign using Histogram of Oriented Gradients (HOG) and Colorspace based features. Support Vector Machines (SVM) is used for classifying images.
Attendence system using Artificial Intelligence - A collaboration project by team Mavericks
Detection algorithms and applications from famous papers; simple theory; solid code.
vehicle detection by HOG and color features
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.
Fast computation of rectangular histogram of oriented gradients (R-HOG) features using integral histogram
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.
Traffic sign detection and classification
🖐 An implementation of a machine learning model for detecting and recognizing hand signs (0-5) accurately using Python. The project pipeline involves the following modules: Preprocessing, Feature Extraction, Model selection and training, and finally performance analysis.