Data Harvesting and Event Detection from Czech Twitter

2018 ◽  
pp. 102-115
Author(s):  
Václav Rajtmajer ◽  
Pavel Král
Keyword(s):  
Event Detection

Military situation awareness: Facilitating critical event detection in chat

2006 ◽  
Author(s):  
Jean M. Catanzaro ◽  
Matthew R. Risser ◽  
John W. Gwynne ◽  
Daniel I. Manes
Keyword(s):  
Situation Awareness ◽  
Event Detection ◽  
Critical Event

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.

Tweets analysis for event detection

2016 ◽  
Vol 21 (1) ◽  
pp. 61-80
Author(s):  
Soumaya Cherichi ◽  
Rim Faiz
Keyword(s):  
Event Detection

Saliency detection in deep learning era: trends of development

2019 ◽  
pp. 10-36 ◽  
Author(s):  
M. N. Favorskaya ◽  
L. C. Jain
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Event Detection ◽  
Visual Analysis ◽  
Saliency Detection ◽  
Salient Object Detection ◽  
Public Image ◽  
Detection Methods ◽  
Salient Object ◽  
Salient Event

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.

Semantic Trajectory Based Video Event Detection

2010 ◽  
Vol 33 (10) ◽  
pp. 1845-1858
Author(s):  
Xiao-Feng WANG ◽  
Da-Peng ZHANG ◽  
Fei WANG ◽  
Zhong-Zhi SHI
Keyword(s):  
Event Detection ◽  
Video Event ◽  
Video Event Detection

Method research of new event detection based on news element

2009 ◽  
Vol 28 (11) ◽  
pp. 2975-2977 ◽  
Author(s):  
Xiao-fei XUE ◽  
Yong-kui ZHANG ◽  
Xiao-dong REN
Keyword(s):  
Event Detection

Auroral event detection using spatiotemporal statistics of local motion vectors

2013 ◽  
Vol 24 (3) ◽  
pp. 175 ◽  
Author(s):  
Qian WANG ◽  
Jimin LIANG ◽  
Zejun HU
Keyword(s):  
Event Detection ◽  
Local Motion ◽  
Motion Vectors
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