Paper Info
Title
Analytics in real time surveillance video using two-bit transform accelerative regressive frame check
Abstract
Face recognition is an established area of research in computer vision and it had played a great role in developing content based personal retrieval systems from real time surveillance video feeds. Face recognition in live videos is a complex problem as facial features fall into high dimensional space and involves large search time. Though, there is an extensive improvement in computational infrastructure over the years, the need for improved search algorithms without increase in cost is a challenge. Existingmethodologies in literature fail to perform in real time scenarios as the cost of feature matching is high. Hence, this research work proposes a Two-Bit Transform AccelerativeRegressive Frame Check algorithm (2BT-ARFCA) methodology that facilitates face recognition in video at a faster rate, suitable for surveillance and authentication applications. Finally the results are experimentally validated with variousvideo datasets and the state-of-the-art techniques proves that the proposed method performs better in terms of Specificity, Sensitivity, Mean Square Error (MSE), Peak signal to noise Ratio (PSNR), The Structural Similarity Index (SSIM) and accuracy.
Year
DOI
Venue
2020
10.1007/s11042-019-7526-3
Multimedia Tools and Applications
Keywords
DocType
Volume
Accelerative Regressive frame Check algorithm, Video datasets, Peak signal to noise ratio
Journal
79
Issue
ISSN
Citations 
23
1380-7501
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Gunasekaran Manogaran1486.97
S. Baskar213418.57
P. Mohamed Shakeel3102.26
Naveen Chilamkurti492170.03
R. Kumar5248.02