Suspicious Activity Detection in Surveillance Video Using Fully Convolutional Networks Segmentation
S. Santhiya1 , T. Ratha Jeyalakshmi2
Section:Research Paper, Product Type: Journal Paper
Volume-07 ,
Issue-08 , Page no. 139-142, Apr-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7si8.139142
Online published on Apr 10, 2019
Copyright © S. Santhiya, T. Ratha Jeyalakshmi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Citation
IEEE Style Citation: S. Santhiya, T. Ratha Jeyalakshmi, “Suspicious Activity Detection in Surveillance Video Using Fully Convolutional Networks Segmentation,” International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.139-142, 2019.
MLA Citation
MLA Style Citation: S. Santhiya, T. Ratha Jeyalakshmi "Suspicious Activity Detection in Surveillance Video Using Fully Convolutional Networks Segmentation." International Journal of Computer Sciences and Engineering 07.08 (2019): 139-142.
APA Citation
APA Style Citation: S. Santhiya, T. Ratha Jeyalakshmi, (2019). Suspicious Activity Detection in Surveillance Video Using Fully Convolutional Networks Segmentation. International Journal of Computer Sciences and Engineering, 07(08), 139-142.
BibTex Citation
BibTex Style Citation:
@article{Santhiya_2019,
author = {S. Santhiya, T. Ratha Jeyalakshmi},
title = {Suspicious Activity Detection in Surveillance Video Using Fully Convolutional Networks Segmentation},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {4 2019},
volume = {07},
Issue = {08},
month = {4},
year = {2019},
issn = {2347-2693},
pages = {139-142},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=934},
doi = {https://doi.org/10.26438/ijcse/v7i8.139142}
publisher = {IJCSE, Indore, INDIA},
}
RIS Citation
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i8.139142}
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=934
TI - Suspicious Activity Detection in Surveillance Video Using Fully Convolutional Networks Segmentation
T2 - International Journal of Computer Sciences and Engineering
AU - S. Santhiya, T. Ratha Jeyalakshmi
PY - 2019
DA - 2019/04/10
PB - IJCSE, Indore, INDIA
SP - 139-142
IS - 08
VL - 07
SN - 2347-2693
ER -
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Abstract
In Recent Years, suspicious activity detection is used to detect traffic in different surveillance videos with high accuracy and high speed in daytime. This surveillance video detection method includes Adaptive Background, Object Modeling, Object Tracking, Activity Recognition, and Segmentation. The semantic segmentation using suspicious activity detection techniques plays a major role in the segmentation of the surveillance video. U-Net is one of the popular Fully Convolutional Networks (FCN) which is applicable for image segmentation. This method could found the different anomalies activity from the videos.
Key-Words / Index Term
Segmentation, FCN, Object Modeling, Suspicious Activity Detection, Surveillance Video
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