Browsing by Subject "Event detection"

Now showing 1 - 7 of 7

Open Access
Flexible test-bed for unusual behavior detection
(ACM, 2007-07) Petrás I.; Beleznai, C.; Dedeolğu, Yiğithan; Pards, M.; Kovács L.; Szlávik, Z.; Havasi L.; Szirányi, T.; Töreyin, B. Uğur; Güdükbay, Uğur; Çetin, A.hmet Enis; Canton-Ferrer, C.
Visual surveillance and activity analysis is an active research field of computer vision. As a result, there are several different algorithms produced for this purpose. To obtain more robust systems it is desirable to integrate the different algorithms. To help achieve this goal, we propose a flexible, distributed software collaboration framework and present a prototype system for automatic event analysis. Copyright 2007 ACM.
Open Access
Framework for online superimposed event detection by sequential Monte Carlo methods
(IEEE, 2008-03-04) Urfalıoğlu, Onay; Kuruoğlu, E. E.; Çetin, A. Enis
In this paper, we consider online seperation and detection of superimposed events by applying particle filtering. We concentrate on a model where a background process, represented by a 1D-signal, is superimposed by an Auto-Regressive (AR) 'event signal', but the proposed approach is applicable in a more general setting. The activation and deactivation times of the event-signal are assumed to be unknown. We solve the online detection problem of this superpositional event by extending the state space dimension by one. The additional parameter of the state represents the AR-signal, which is zero when deactivated. Numerical experiments demonstrate the effectiveness of our approach. ©2008 IEEE.
Open Access
Metadata extraction from text in soccer domain
(IEEE, 2008-10) Göktürk, Z. O.; Çiçekli, N. K.; Çiçekli, İlyas
Event detection is a crucial part for soccer video searching and querying. The event detection could be done by video content itself or from a structured or semi structured text files gathered from sports web sites. In this paper, we present an approach of metadata extraction from match reports for soccer domain. The UEFA Cup and UEFA Champions League Match Reports are downloaded from the web site of UEFA by a web-crawler. Using regular expressions we annotate these match reports and then extract events from annotated match reports. Extracted events are saved in an MPEG-7 file. We present an interface that is used to query the events in the MPEG-7 match corpus. If an associated match video is available, the video portions that correspond to the found events could be played. © 2008 IEEE.
Open Access
New event detection and topic tracking in Turkish
(John Wiley & Sons, Inc., 2010) Can, F.; Kocberber, S.; Baglioglu, O.; Kardas, S.; Ocalan, H. C.; Uyar, E.
Topic detection and tracking (TDT) applications aim to organize the temporally ordered stories of a news stream according to the events. Two major problems in TDT are new event detection (NED) and topic tracking (TT). These problems focus on finding the first stories of new events and identifying all subsequent stories on a certain topic defined by a small number of sample stories. In this work, we introduce the first large-scale TDT test collection for Turkish, and investigate the NED and TT problems in this language. We present our test-collection-construction approach, which is inspired by the TDT research initiative. We show that in TDT for Turkish with some similarity measures, a simple word truncation stemming method can compete with a lemmatizer-based stemming approach. Our findings show that contrary to our earlier observations on Turkish information retrieval, in NED word stopping has an impact on effectiveness. We demonstrate that the confidence scores of two different similarity measures can be combined in a straightforward manner for higher effectiveness. The influence of several similarity measures on effectiveness also is investigated. We show that it is possible to deploy TT applications in Turkish that can be used in operational settings. © 2010 ASIS&T.
Open Access
On-line new event detection and tracking in a multi-resource environment
(2001) Kurt, Hakan
As the amount of electronically available information resources increase, the need for information also increases. Today, it is almost impossible for a person to keep track all the information resources and find new events as soon as possible. In this thesis, we present an on-line new event detection and tracking system, which automatically detects new events from multiple news resources and immediately start tracking events as they evolve. Since we implemented the on-line version of event detection approach, the novelty decision about a news story is done before processing the next one. We also present a new threshold, called support threshold, used in detection process to decrease the number of new event alarms, that are caused by informative and one-time-only news. The support threshold can be used to tune the weights of news resources. We implemented the tracking phase as an unsupervised learning process, that is, detected events are automatically tracked by training the system using the first news story of an event. Since events evolve over time, an unsupervised adaptation is used to retrain the tracking system in order to increase the tracking system performance. Adaptation is achieved by adding predicted documents to the training process. From the corpus observations, we conclude that one news story can be associated to more than one event. For this reason, the tracking system can relate a news story to more than one event. The on-line new event detection and tracking system has been tested on the Reuters news feed, available on the Internet. The Reuters news feed, that we used, comprises four independent news resources. The news stories are in Turkish.
Open Access
Superimposed event detection by sequential Monte Carlo methods
(IEEE, 2007) Urfalıoğlu, O.; Kuruoğlu, E. E.; Çetin, A. Enis
In this paper, we consider the detection of rare events by applying particle filtering. We model the rare event as an AR signal superposed on a background signal. The activation and deactivation times of the AR-signal are unknown. We solve the online detection problem of this superpositional rare event by extending the state space dimension by one. The additional parameter of the state represents the AR-signal, which is zero when deactivated. Numerical experiments demonstrate the effectiveness of our approach.
Open Access
Transforming temporal-dynamic graphs into time-series data for solving event detection problems
(TÜBİTAK, 2023-09-29) Taşcı, Kutay; Akal, Fuat
Event detection on temporal-dynamic graphs aims at detecting significant events based on deviations from the normal behavior of the graphs. With the widespread use of social media, many real-world events manifest as social media interactions, making them suitable for modeling as temporal-dynamic graphs. This paper presents a workflow for event detection on temporal-dynamic graphs using graph representation learning. Our workflow leverages generated embeddings of a temporal-dynamic graph to reframe the problem as an unsupervised time-series anomaly detection task. We evaluated our workflow on four distinct real-world social media datasets and compared our results with the related work. The results show that the performance depends on how anomalies deviate from normal. These include changes in both size and topology. Our results are similar to the related work for the graphs where the deviation from a normal state of the temporal-dynamic graph is apparent, e.g., Reddit. On the other hand, we achieved a 3-fold improvement in precision for the graphs where deviations exist on size and topology, e.g., Twitter. Also, our results are 20% to 5-fold better even if we introduced some delay factor. That is, we beat our competition while detecting events that occurred some time ago. As a result, our study proves that graph embeddings as time-series data can be used for event detection tasks.