Open AccessMachine Learning Approach to Detect Drowsiness on Behavioral Parameters
Author(s) -
R Aannd,
AUTHOR_ID,
Gayathri Anil,
Rishika Sankaran,
Anushruti Adhikari,
Kruthika Ravishankar,
AUTHOR_ID,
AUTHOR_ID,
AUTHOR_ID,
AUTHOR_ID
Publication year - 2022
Publication title -
ymer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.103
H-Index - 5
ISSN - 0044-0477
DOI - 10.37896/ymer21.01/01
Subject(s) - computer science , object detection , object (grammar) , artificial intelligence , pedestrian detection , machine learning , interface (matter) , human–computer interaction , face (sociological concept) , computer vision , pedestrian , pattern recognition (psychology) , engineering , social science , bubble , maximum bubble pressure method , parallel computing , sociology , transport engineering
Object detection has received a lot of research attention in recent years because of its tight association with video analysis and picture interpretation. Face detection, vehicle detection, pedestrian counting, web photos, security systems, and self-driving automobiles are all examples of object detection. With little conscious thought, the human visual system can accomplish complicated tasks such as distinguishing multiple objects and detecting impediments. Thanks to the availability of large amounts of data, faster GPUs, and improved algorithms, we can now quickly train computers to detect and classify many elements inside a picture with high accuracy. Our project is focused on building a single-access platform for various object detection tasks. A user-interface where the user is asked for the relevant inputs and an output based on this is generated automatically by the system. Also, accuracy and precision measures are also displayed so that the user is wary of their liability extent on the generated results.