@article{(Open Science Index):https://publications.waset.org/pdf/10008273,
	  title     = {Automated Video Surveillance System for Detection of Suspicious Activities during Academic Offline Examination},
	  author    = {G. Sandhya Devi and  G. Suvarna Kumar and  S. Chandini },
	  country	= {},
	  institution	= {},
	  abstract     = {This research work aims to develop a system that will analyze and identify students who indulge in malpractices/suspicious activities during the course of an academic offline examination. Automated Video Surveillance provides an optimal solution which helps in monitoring the students and identifying the malpractice event immediately. This work is organized into three modules. The first module deals with performing an impersonation check using a PCA-based face recognition method which is done by cross checking his profile with the database. The presence or absence of the student is even determined in this module by implementing an image registration technique wherein a grid is formed by considering all the images registered using the frontal camera at the determined positions. Second, detecting such facial malpractices in which a student gets involved in conversation with another, trying to obtain unauthorized information etc., based on the threshold range evaluated by considering his/her mouth state whether open or closed. The third module deals with identification of unauthorized material or gadgets used in the examination hall by training the positive samples of the object through various stages. Here, a top view camera feed is analyzed to detect the suspicious activities. The system automatically alerts the administration when any suspicious activities are identified, thereby reducing the error rate caused due to manual monitoring. This work is an improvement over our previous work published in identifying suspicious activities done by examinees in an offline examination.
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {11},
	  number    = {12},
	  year      = {2017},
	  pages     = {1265 - 1271},
	  ee        = {https://publications.waset.org/pdf/10008273},
	  url   	= {https://publications.waset.org/vol/132},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 132, 2017},
	}