How not to drown in a sea of information: An event recognition approach

2015 ◽  
Author(s):  
Elias Alevizos ◽  
Alexander Artikis ◽  
Kostas Patroumpas ◽  
Marios Vodas ◽  
Yannis Theodoridis ◽  
...  
Keyword(s):  
Event Recognition

Confidence Inflation and Event Recognition With Young and Older Adult Eyewitnesses

2004 ◽  
Author(s):  
Jeffrey S. Neuschatz ◽  
Michael P. Toglia ◽  
Elizabeth L. Preston ◽  
James M. Lampinen ◽  
Joseph S. Neuschatz ◽  
...  
Keyword(s):  
Older Adult ◽  
Event Recognition

scholarly journals Olympic Games Event Recognition via Transfer Learning with Photobombing Guided Data Augmentation

2021 ◽  
Vol 7 (2) ◽  
pp. 12
Author(s):  
Yousef I. Mohamad ◽  
Samah S. Baraheem ◽  
Tam V. Nguyen
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Olympic Games ◽  
Event Recognition ◽  
Surveillance Systems ◽  
Video Captioning ◽  
Practical Applications ◽  
Sport Events ◽  
The Olympic Games

Automatic event recognition in sports photos is both an interesting and valuable research topic in the field of computer vision and deep learning. With the rapid increase and the explosive spread of data, which is being captured momentarily, the need for fast and precise access to the right information has become a challenging task with considerable importance for multiple practical applications, i.e., sports image and video search, sport data analysis, healthcare monitoring applications, monitoring and surveillance systems for indoor and outdoor activities, and video captioning. In this paper, we evaluate different deep learning models in recognizing and interpreting the sport events in the Olympic Games. To this end, we collect a dataset dubbed Olympic Games Event Image Dataset (OGED) including 10 different sport events scheduled for the Olympic Games Tokyo 2020. Then, the transfer learning is applied on three popular deep convolutional neural network architectures, namely, AlexNet, VGG-16 and ResNet-50 along with various data augmentation methods. Extensive experiments show that ResNet-50 with the proposed photobombing guided data augmentation achieves 90% in terms of accuracy.

Sound event recognition through expectancy-based evaluation ofsignal-driven hypotheses

2010 ◽  
Vol 31 (12) ◽  
pp. 1552-1559 ◽  
Author(s):  
J.D. Krijnders ◽  
M.E. Niessen ◽  
T.C. Andringa
Keyword(s):  
Event Recognition ◽  
Sound Event

An approach to mechanistic event recognition applied on monitoring organic matter depletion in SBRs

AIChE Journal ◽  
2014 ◽  
Vol 60 (10) ◽  
pp. 3460-3472 ◽  
Author(s):  
Mariano N. Cruz Bournazou ◽  
Stefan Junne ◽  
Peter Neubauer ◽  
Tilman Barz ◽  
Harvey Arellano-Garcia ◽  
...  
Keyword(s):  
Organic Matter ◽  
Event Recognition

Dagstuhl Seminar on the Foundations of Composite Event Recognition

2021 ◽  
Vol 49 (4) ◽  
pp. 24-27
Author(s):  
Alexander Artikis ◽  
Thomas Eiter ◽  
Alessandro Margara ◽  
Stijn Vansummeren
Keyword(s):  
Event Recognition ◽  
Event Data ◽  
Composite Event ◽  
Geographically Distributed

Composite event recognition (CER) is concerned with continuously matching patterns in streams of 'event' data over (geographically) distributed sources. This paper reports the results of the Dagstuhl Seminar "Foundations of Composite Event Recognition" held in 2020.

A Formal Framework for Complex Event Recognition

2021 ◽  
Vol 46 (4) ◽  
pp. 1-49
Author(s):  
Alejandro Grez ◽  
Cristian Riveros ◽  
Martín Ugarte ◽  
Stijn Vansummeren
Keyword(s):  
Query Languages ◽  
Event Recognition ◽  
Distributed Data ◽  
Performance Guarantees ◽  
Selection Strategies ◽  
Trade Offs ◽  
Formal Framework ◽  
Linear Delay ◽  
Complex Events ◽  
Complex Event Recognition

Complex event recognition (CER) has emerged as the unifying field for technologies that require processing and correlating distributed data sources in real time. CER finds applications in diverse domains, which has resulted in a large number of proposals for expressing and processing complex events. Existing CER languages lack a clear semantics, however, which makes them hard to understand and generalize. Moreover, there are no general techniques for evaluating CER query languages with clear performance guarantees. In this article, we embark on the task of giving a rigorous and efficient framework to CER. We propose a formal language for specifying complex events, called complex event logic (CEL), that contains the main features used in the literature and has a denotational and compositional semantics. We also formalize the so-called selection strategies, which had only been presented as by-design extensions to existing frameworks. We give insight into the language design trade-offs regarding the strict sequencing operators of CEL and selection strategies. With a well-defined semantics at hand, we discuss how to efficiently process complex events by evaluating CEL formulas with unary filters. We start by introducing a formal computational model for CER, called complex event automata (CEA), and study how to compile CEL formulas with unary filters into CEA. Furthermore, we provide efficient algorithms for evaluating CEA over event streams using constant time per event followed by output-linear delay enumeration of the results.

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