Automated Event Recognition for Football Commentary Generation

2014 ◽  
pp. 300-315
Author(s):  
Maliang Zheng ◽  
Daniel Kudenko
Keyword(s):  
Real Time ◽  
Inductive Learning ◽  
Automatic Generation ◽  
Event Recognition ◽  
Dangerous Situation ◽  
Learning Techniques

The enjoyment of many games can be enhanced by in-game commentaries. In this paper, the authors focus on the automatic generation of commentaries for football games, using Championship Manager as a case study. The basis of this approach is a real-time mapping of game states to commentary concepts, such as “dangerous situation for team A”. While in some cases it is feasible to provide such a mapping by hand-coding, in some cases it is not straight-forward because the meaning of the concepts cannot be easily formalized. In these cases, the authors propose to use inductive learning techniques that learn such a mapping from annotated game traces.

Automated Event Recognition for Football Commentary Generation

2010 ◽  
Vol 2 (4) ◽  
pp. 67-84 ◽  
Author(s):  
Maliang Zheng ◽  
Daniel Kudenko
Keyword(s):  
Real Time ◽  
Inductive Learning ◽  
Automatic Generation ◽  
Event Recognition ◽  
Dangerous Situation ◽  
Learning Techniques

The enjoyment of many games can be enhanced by in-game commentaries. In this paper, the authors focus on the automatic generation of commentaries for football games, using Championship Manager as a case study. The basis of this approach is a real-time mapping of game states to commentary concepts, such as “dangerous situation for team A”. While in some cases it is feasible to provide such a mapping by hand-coding, in some cases it is not straight-forward because the meaning of the concepts cannot be easily formalized. In these cases, the authors propose to use inductive learning techniques that learn such a mapping from annotated game traces.

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

Pollution based real time control strategies for combined sewer systems

1997 ◽  
Vol 36 (8-9) ◽  
pp. 331-336 ◽  
Author(s):  
Gabriela Weinreich ◽  
Wolfgang Schilling ◽  
Ane Birkely ◽  
Tallak Moland
Keyword(s):  
Real Time ◽  
Control Strategies ◽  
Ammonia Nitrogen ◽  
Simple Extension ◽  
Real Time Control ◽  
Time Control ◽  
Sewer Systems ◽  
Receiving Waters ◽  
Tunnel System

This paper presents results from an application of a newly developed simulation tool for pollution based real time control (PBRTC) of urban drainage systems. The Oslo interceptor tunnel is used as a case study. The paper focuses on the reduction of total phosphorus Ptot and ammonia-nitrogen NH4-N overflow loads into the receiving waters by means of optimized operation of the tunnel system. With PBRTC the total reduction of the Ptot load is 48% and of the NH4-N load 51%. Compared to the volume based RTC scenario the reductions are 11% and 15%, respectively. These further reductions could be achieved with a relatively simple extension of the operation strategy.

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