REAL-TIME STUDENT SURVEILLANCE SYSTEM USING MACHINE LEARNING AND COMPUTER VISION

In a classroom full of students, it is practically not possible for a single teacher to give personal attention to every student. Continuous monitoring and identification of students who show signs of lethargy, sadness or anger in classrooms can help management counsel them and advice some preventiv...

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Bibliographic Details
Published inInternational journal of advanced research in computer science Vol. 10; no. 4; pp. 29 - 33
Main Author Mehta, Rajat
Format Journal Article
LanguageEnglish
Published Udaipur International Journal of Advanced Research in Computer Science 20.08.2019
Subjects
Algorithms
Artificial intelligence
Automation
Classrooms
Computer vision
Emotion recognition
Face recognition
Histograms
Lethargy
Machine learning
Students
Surveillance
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Summary:In a classroom full of students, it is practically not possible for a single teacher to give personal attention to every student. Continuous monitoring and identification of students who show signs of lethargy, sadness or anger in classrooms can help management counsel them and advice some preventive measures which if kept unchecked can lead the students to take adverse steps. Potential students who need help in some form can be identified. There are some unproductive activities that take place in a classroom which can be easily automated and the time saved can be devoted to productive activities. Manual attendance calling is one of them which can be automated using facial recognition algorithms. In this paper, a robust surveillance system using Machine Learning and Computer Vision algorithms is presented that can take on the above challenges. For facial recognition, Local Binary Pattern Histograms have been used and for emotion recognition, Deep Learning model has been used.
ISSN:0976-5697
0976-5697
DOI:10.26483/ijarcs.v10i4.6445