scholarly journals Sparseyâ„¢: event recognition via deep hierarchical sparse distributed codes

2014 ◽  
Vol 8 ◽  
Author(s):  
Gerard J. Rinkus
Keyword(s):  
Event Recognition ◽  
Distributed Codes

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.

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

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.

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