Rakshana Fathima NR. Pravin KumaarM HossainG MuhammadL JinX XieN KodaliS ParvathaneniR RanjanM PantA ReddyK ReddyH ZhangY XuJ Liu
Finding sleepy drivers is crucial to maintaining traffic safety.Machine learning techniques have been used in a number of recent proposals to identify driver drowsiness.In this research, we provide a technique for OpenCV and Kerasbased driver drowsiness detection.The suggested technique employs a camera to record a live video feed of the driver's face.The required features, such as eye and mouth movements, are then extracted from the video frames using OpenCV preprocessing.Then, a deep learning model based on the Keras framework is trained using the extracted features.A sizable dataset of films documenting driver attentiveness and tiredness is used to train the model.The model is used to forecast the driver's level of tiredness using the retrieved features after it has been trained.
S. AmuthaPydipati VamsiKandi Manikanta ReddyChekka MaheshPilli Vamsi Krishna ReddyK. Ashok Reddy
R. VishnuS. VishvaragulP. SrihariR. NithiavathyRaj Shree
Mayank SrivastavaShoyab Alam IdrisiTushar Gupta
Vedant KaushishNitesh Kumar SinghKunal MaggoPreeti NagrathRachna Jain
R. Syed Ali FathimaK KeerthiK. BhuvaneshK. Naga JyothiK Raga SravyaPraveen Kumar