Udacity CarND - Vehicle Detection and Tracking
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Updated
Mar 7, 2017 - Python
Udacity CarND - Vehicle Detection and Tracking
car detection and tracking
detect and track vehicles in a video, taken with a front facing camera mounted on a car
Udacity Self Driving Car Engineer Project - Vehicle Detection using HOG
Vehicle detection based on YOLO and SVM
Face Recognition library
HOG-based linear SVM for detecting vehicles (or any other object) in videos
A live drowsiness detection system made to run on a single board computer like raspi. It has been tested with different extreme parameters of distance and spectacles. Can be a huge product to run on cars and prevent accidents as the system runs with 0% internet connectivity.
Detects facial landmarks in real-time from the input feed of a webcam/usb-camera/pi-cam using a HOG + Linear SVM model.
Detect different parts of the face such as, eyes brows, nose, jaw, etc. individually
Training SVM classifier to recognize people expressions (emotions) on Fer2013 dataset
Computer Vision Driverless car object detection assignment
This project uses Histogram of Oriented Gradients for pedestrian detection and Kalman Filter for tracking and prediction
Detect eye blinks based on eye aspect ratio (EAR) introduced by Soukupová and Čech in their 2016 paper, Real-Time Eye Blink Detection Using Facial Landmarks.
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