EXAM SURVEILLANCE USING MACHINE LEARNING
Kothapalli Sreeja1, Mallipalli Spurthi Reddy2, N Ashok3, N Bhavya4,Prof. Beena K5
1 Kothapalli Sreeja Computer Science and Engineering, K.S. Institute of Technology, Bangalore, India
2 Mallipalli Spurthi Reddy Computer Science and Engineering, K.S. Institute of Technology, Bangalore, India
3 N Ashok Computer Science and Engineering, K.S. Institute of Technology, Bangalore, India
4 N Bhavya Computer Science and Engineering, K.S. Institute of Technology, Bangalore, India
5 Prof. Beena K, Computer Science and Engineering, K.S. Institute of Technology, Bangalore, India
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Abstract - Recent publications on video surveillance systems in conferences and journals show that researchers are paying attention. This review's objectives are to look at the most recent works that have been published in journals, suggest a new classification scheme for video surveillance systems, and look into each component of that scheme. Using the article's title and keyword, this paper presents a thorough and organized analysis of the prior literature on video surveillance systems from 2010 to 2011. The review was taken from six online digital libraries. The architecture of video surveillance systems, which consists of six layers: the Concept and Foundation Layer, Network Infrastructure Layer, Processor Layer, Communication Layer, Application Layer, and User Interaction Layer, serves as the foundation for the suggested classification framework. This analysis demonstrates that while real-time aspects of the problem are the subject of many publications and studies, the use of extracted and retrieved information for forensic video surveillance has received less attention.
Key Words: CCTV, video surveillance system, exam surveillance, student activity recognition, frameworks, datasets, network infrastructure layer, processing layer, communication layer, application layer, and user interaction layer.
