Wedad55's repositories
CSE4062S20_Grp7
Data Science / Computer Vision Project: Driver Drowsiness Detection
Driver-Drowsiness-Detection-14
An Open-CV based python apllication which indicate whether the Driver is sleepy or not with various levels of Alerts.
Driver-Drowsiness-Detection-system-2
Automated system to alarm the driver from drowsiness conditions
Driver-Sleep-Detection-Face-Eyes-Mouth-Detection
As part of my thesis project, I designed a monitoring system in Matlab which processes the video input to indicate the current driving aptitude of the driver and warning alarm is raised based on eye blink and mouth yawning rate if driver is fatigue. It is implemented using Viola-Jones and Sobel techniques for facial features detection.
Driver_sleep_detection_in_Python
This project uses computer vision and cnn model to identify if a driver is sleepy while driving .
Drowsy-Driver-Detection-System
System to detect driver drowsiness using HOG facial landmarks
DrowsyDriverDetection
Drowsy driver detection using Keras and convolution neural networks.
haar-cascade-files
A complete collection of Haar-Cascade files. Every Haar-Cascades here!
image_class
基于keras集成多种图像分类模型： VGG16、VGG19、InceptionV3、Xception、MobileNet、AlexNet、LeNet、ZF_Net、ResNet18、ResNet34、ResNet50、ResNet_101、ResNet_152、DenseNet
MSc-Project-Part1-Pneumonia-detection-using-tansfer-learning-on-ResNet50-VGG16-and-VGG19
The dataset having Pneumonia and Normal chest X-Ray images were trained on different numbers of epochs to check the variability in the training and validation accuracies. The ResNet50 model with the highest and closest Training and Validation accuracies was then used for the prediction.
py-feat
Facial Expression Analysis Toolbox
sleepy_driver
Detect sleepy drivers
TeethClassifierCNN
This classifier displays a green box around a person's face if it detects teeth.
Yawn-Detection-OpenCV-Keras-TensorFlow-Sklearn
This repository contains Python code for generating a yawning detection model and using it to detect yawning instances from a live camera stream. The model architecture consists of convolutional and pooling layers, followed by fully connected layers.