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Limited By:	Subject = Event Detection;

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Displaying Results 1 - 6 of 6 on page 1 of 1

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A framework for event detection in field-sports video broadcasts based on SVM generated audio-visual feature model. Case-study: soccer video (2004)

Sadlier, David A.; O'Connor, Noel E.; Murphy, Noel; Marlow, Seán

A framework for event detection in field-sports video broadcasts based on SVM generated audio-visual feature model. Case-study: soccer video (2004)

Sadlier, David A.; O'Connor, Noel E.; Murphy, Noel; Marlow, Seán

Abstract:

In this paper we propose a novel audio-visual feature-based framework, for event detection in field sports broadcast video. The system is evaluated via a case-study involving MPEG encoded soccer video. Specifically, the evidence gathered by various feature detectors is combined by means of a learning algorithm (a support vector machine), which infers the occurrence of an event, based on a model generated during a training phase, utilizing a corpus of 25 hours of content. The system is evaluated using 25 hours of separate test content. Following an evaluation of results obtained, it is shown for this case, that both high precision and recall statistics are achievable.

http://doras.dcu.ie/399/

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A Speech Based Approach to Surveillance Video Retrieval (2014)

QasemiZadeh, Behrang; O'Neill, Ian; Shen, Jiali; Miller, Paul; Hanna, Philip; Stwa...

A Speech Based Approach to Surveillance Video Retrieval (2014)

QasemiZadeh, Behrang; O'Neill, Ian; Shen, Jiali; Miller, Paul; Hanna, Philip; Stwart, Darryl; Wang, Hongbin

Abstract:

This paper describes the anatomy of a pilot surveillance system with a speech-based interface for content-based retrieval of video data. The proposed system relies on an ontology-based information sharing architecture and lets components of the system communicate among each other through TCP/IP communication channels. The aim of developing the pilot system was to explore dependencies between image analysis, event detection, video annotation, and speech- based retrieval of the video content in the context of a broader spoken dialogue system

http://www.deri.ie/sites/default/files/publications/bq_speech_based.pdf

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Discovering News Events That Move Markets (2017)

Gurin, Yuriy; Szymanski, Terrence; Keane, Mark T.

Discovering News Events That Move Markets (2017)

Gurin, Yuriy; Szymanski, Terrence; Keane, Mark T.

Abstract:

Intelligent Systems Conference 2017 (IntelliSys2017), London, United Kingdom, 7-8 September 2017
Recently, there has been an explosion of interest in the use of textual sources (e.g., market reports, news articles, company reports) to predict changes in stock and commodity markets. Most of this research is on sentiment analysis, but some of it has tried to use the news itself to predict market movements. In this paper, we use 10-years of news articles – from a weekly, agricultural, trade newspaper – to predict price changes in a commodity market for beef. Two experiments explore the different ways in which news reports affect the market via (i) major market-impacting events (i.e., rare natural disasters or food scandals) or (ii) minor market-impacting events (e.g., mundane reports about inflation, oil prices, etc). We find that different techniques need to be used to uncover major events (e.g., LLRs) as opposed to minor events (e.g., classifiers) and show that no single unified ...

http://hdl.handle.net/10197/9060

Marked Mark

Event Detection in Twitter using Aggressive Filtering and Hierarchical Tweet Clustering (2016)

Ifrim, Georgiana; Shi, Bichen; Brigadir, Igor

Event Detection in Twitter using Aggressive Filtering and Hierarchical Tweet Clustering (2016)

Ifrim, Georgiana; Shi, Bichen; Brigadir, Igor

Abstract:

Second Workshop on Social News on the Web (SNOW), Seoul, Korea, 8 April 2014
Twitter has become as much of a news media as a social network, and much research has turned to analysing its content for tracking real-world events, from politics to sports and natural disasters. This paper describes the techniques we employed for the SNOW Data Challenge 2014, described in [16]. We show that aggressive lettering of tweets based on length and structure, combined with hierarchical clustering of tweets and ranking of the resulting clusters, achieves encouraging results. We present empirical results and discussion for two different Twitter streams focusing on the US presidential elections in 2012 and the recent events about Ukraine, Syria and the Bitcoin, in February 2014.
Science Foundation Ireland

http://hdl.handle.net/10197/7546

Marked Mark

Towards Social Event Detection and Contextualisation for Journalists (2014)

Khare, Prashant; Heravi, Bahareh Rahmanzadeh

Towards Social Event Detection and Contextualisation for Journalists (2014)

Khare, Prashant; Heravi, Bahareh Rahmanzadeh

Abstract:

Conference paper
Social media platforms have become an important source of information in course of a break- ing news event, such as natural calamity, political uproar, etc. News organisations and journal- ists are increasingly realising the value of information being propagated via social media. However, the sheer volume of the data produced on social media is overwhelming and manual inspection of this streaming data for finding, aggregation, and contextualising emerging event in a short time span is a day-to-day challenge by journalists and media organisations. It high- lights the need for better tools and methods to help them utilise this user generated information for news production. This paper addresses the above problem for journalists by proposing an event detection and contextualisation framework that receives an input stream of social media data and generates the likely events in the form of clusters along with a certain context.

http://hdl.handle.net/10379/4655

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Where is the News Breaking? Towards a Location-based Event Detection Framework for Journalists (2014)

Khare, Prashant; Heravi, Bahareh Rahmanzadeh

Where is the News Breaking? Towards a Location-based Event Detection Framework for Journalists (2014)

Khare, Prashant; Heravi, Bahareh Rahmanzadeh

Abstract:

Conference paper (published)
The rise of user-generated content (UCG) as a source of information in the journalistic lifecycle is driving the need for automated methods to detect, filter, contextualise and verify citizen reports of breaking news events. In this position paper we outline the technological challenges in incorporating UCG into news reporting and describe our proposed framework for exploiting UGC from social media for location-based event detection and filtering to reduce the workload of journalists covering breaking and ongoing news events. News organisations increasingly rely on manually curated UGC. Manual monitoring, filtering, verification and curation of UGC, however, is a time and effort consuming task, and our proposed framework takes a first step in addressing many of the issues surrounding these processes.

http://hdl.handle.net/10379/4656

Displaying Results 1 - 6 of 6 on page 1 of 1