Traffic reduction via localized video stream on service-oriented optical Internet

2011 ◽  
Author(s):  
Li Hui ◽  
Gao Yuan ◽  
Ji Yuefeng
Keyword(s):  
Video Stream ◽  
Optical Internet ◽  
Traffic Reduction ◽  
Service Oriented

Analysis and experimentation of key technologies in service-oriented optical internet

2011 ◽  
Vol 54 (2) ◽  
pp. 215-226 ◽  
Author(s):  
YueFeng Ji ◽  
DanPing Ren ◽  
Hui Li ◽  
XueJun Liu ◽  
ZhengZhong Wang
Keyword(s):  
Optical Internet ◽  
Key Technologies ◽  
Service Oriented

An adaptive virtual topology reconfiguration policy in multi-wavelengt optical Internet

2003 ◽  
Vol 14 (5) ◽  
pp. 417-422 ◽  
Author(s):  
Kyunghee Lee ◽  
Lin Zhang ◽  
Chan-Hyun Youn
Keyword(s):  
Virtual Topology ◽  
Optical Internet

Framework for rare event detection using Artificial Neural Network based context free grammar

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
Performance Metrics ◽  
Rare Events ◽  
Rare Event ◽  
Video Stream ◽  
Context Free Grammar ◽  
Artificial Neural ◽  
Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

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