Research on Convolutional Neural Network for Object Classification in Outdoor Video Surveillance System

2019 ◽  
pp. 221-229 ◽  
Author(s):  
I. S. Fomin ◽  
A. V. Bakhshiev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Video Surveillance ◽  
Surveillance System ◽  
Object Classification ◽  
Video Surveillance System

An Efficient Optimal Neural Network-Based Moving Vehicle Detection in Traffic Video Surveillance System

2019 ◽  
Vol 39 (2) ◽  
pp. 734-756 ◽  
Author(s):  
Ahilan Appathurai ◽  
Revathi Sundarasekar ◽  
C. Raja ◽  
E. John Alex ◽  
C. Anna Palagan ◽  
...  
Keyword(s):  
Neural Network ◽  
Video Surveillance ◽  
Surveillance System ◽  
Vehicle Detection ◽  
Video Surveillance System ◽  
Moving Vehicle ◽  
Traffic Video ◽  
Moving Vehicle Detection

Image Captioning for Video Surveillance System using Neural Networks

2021 ◽  
pp. 2150044
Author(s):  
M. Nivedita ◽  
Priyanka Chandrashekar ◽  
Shibani Mahapatra ◽  
Y. Asnath Victy Phamila ◽  
Sathish Kumar Selvaperumal
Keyword(s):  
Neural Network ◽  
Video Surveillance ◽  
Surveillance System ◽  
Short Term Memory ◽  
Low Cost ◽  
Modern Technology ◽  
Discrete Wavelet ◽  
Video Surveillance System ◽  
Image Captioning ◽  
Video Feed

Security has always been of paramount importance to humans. In the absence of a sense of security at one’s workplace, home or anywhere else, people feel uneasy and vulnerable. With the improvement of modern technology, along with the lack of time at hand, the need for faster, efficient, accurate as well as low-cost security techniques is more than ever. Image Captioning for Video Surveillance System is proposed to develop visual systems that generate contextual descriptions about objects in images, and then use these descriptions to provide information of the scene that needs to be secured. The proposed system uses a neural network model composed of a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to caption the incoming video feed. The main significance of this paper is in integrating the system with Discrete Wavelet Transform (DWT), which is applied on the incoming video feed, so that the compressed LL band frames transferred wirelessly to the model are smaller in comparison, leading to less transfer time and faster processing by the model.

Video Surveillance System for Elderly Person Living Alone by Person Tracking and Fall Detection

2006 ◽  
Vol 126 (8) ◽  
pp. 457-463 ◽  
Author(s):  
Motonori Doi ◽  
Hiroshi Inoue ◽  
Yutaro Aoki ◽  
Osamu Oshiro
Keyword(s):  
Video Surveillance ◽  
Surveillance System ◽  
Elderly Person ◽  
Fall Detection ◽  
Living Alone ◽  
Video Surveillance System ◽  
Person Tracking

A Video Surveillance System for Monitoring the Endangered Mediterranean Monk Seal (Monachus monachus)

2007 ◽  
Vol 33 (2) ◽  
pp. 179-184 ◽  
Author(s):  
Panagiotis Dendrinos ◽  
Eleni Tounta ◽  
Alexandros A. Karamanlidis ◽  
Anastasios Legakis ◽  
Spyros Kotomatas
Keyword(s):  
Video Surveillance ◽  
Surveillance System ◽  
Video Surveillance System

A Study on Optimization of Intelligent Video Surveillance System based on Embedded Module

2018 ◽  
Vol 7 (2) ◽  
pp. 40-46 ◽  
Author(s):  
Jin Su Kim ◽  
◽  
Min-Gu Kim ◽  
Sung Bum Pan ◽  
◽  
...  
Keyword(s):  
Video Surveillance ◽  
Surveillance System ◽  
Video Surveillance System ◽  
Intelligent Video Surveillance ◽  
Intelligent Video Surveillance System

scholarly journals Secure Video Surveillance Framework in Smart City

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4419
Author(s):  
Hao Li ◽  
Tianhao Xiezhang ◽  
Cheng Yang ◽  
Lianbing Deng ◽  
Peng Yi
Keyword(s):  
Video Surveillance ◽  
Smart City ◽  
Key Management ◽  
Surveillance System ◽  
Chinese Remainder Theorem ◽  
Smart Cities ◽  
Group Key Management ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Key Update

In the construction process of smart cities, more and more video surveillance systems have been deployed for traffic, office buildings, shopping malls, and families. Thus, the security of video surveillance systems has attracted more attention. At present, many researchers focus on how to select the region of interest (RoI) accurately and then realize privacy protection in videos by selective encryption. However, relatively few researchers focus on building a security framework by analyzing the security of a video surveillance system from the system and data life cycle. By analyzing the surveillance video protection and the attack surface of a video surveillance system in a smart city, we constructed a secure surveillance framework in this manuscript. In the secure framework, a secure video surveillance model is proposed, and a secure authentication protocol that can resist man-in-the-middle attacks (MITM) and replay attacks is implemented. For the management of the video encryption key, we introduced the Chinese remainder theorem (CRT) on the basis of group key management to provide an efficient and secure key update. In addition, we built a decryption suite based on transparent encryption to ensure the security of the decryption environment. The security analysis proved that our system can guarantee the forward and backward security of the key update. In the experiment environment, the average decryption speed of our system can reach 91.47 Mb/s, which can meet the real-time requirement of practical applications.

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