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Relevant bibliographies by topics / Events Detection and Tracking

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Academic literature on the topic 'Events Detection and Tracking'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Events Detection and Tracking"

1

Mamo, Nicholas, Joel Azzopardi, and Colin Layfield. "An Automatic Participant Detection Framework for Event Tracking on Twitter." Algorithms 14, no. 3 (2021): 92. http://dx.doi.org/10.3390/a14030092.

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Topic Detection and Tracking (TDT) on Twitter emulates human identifying developments in events from a stream of tweets, but while event participants are important for humans to understand what happens during events, machines have no knowledge of them. Our evaluation on football matches and basketball games shows that identifying event participants from tweets is a difficult problem exacerbated by Twitter’s noise and bias. As a result, traditional Named Entity Recognition (NER) approaches struggle to identify participants from the pre-event Twitter stream. To overcome these challenges, we desc

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Duo, Jingyun, and Long Zhao. "An Asynchronous Real-Time Corner Extraction and Tracking Algorithm for Event Camera." Sensors 21, no. 4 (2021): 1475. http://dx.doi.org/10.3390/s21041475.

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Event cameras have many advantages over conventional frame-based cameras, such as high temporal resolution, low latency and high dynamic range. However, state-of-the-art event- based algorithms either require too much computation time or have poor accuracy performance. In this paper, we propose an asynchronous real-time corner extraction and tracking algorithm for an event camera. Our primary motivation focuses on enhancing the accuracy of corner detection and tracking while ensuring computational efficiency. Firstly, according to the polarities of the events, a simple yet effective filter is

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Dekoninck, Luc. "Detecting and Correlating Aircraft Noise Events below Ambient Noise Levels Using OpenSky Tracking Data." Proceedings 59, no. 1 (2020): 13. http://dx.doi.org/10.3390/proceedings2020059013.

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Noise annoyance due to aircraft operations extends well beyond the 55 Lden noise contours as calculated according to the Environmental Noise Directive (END). Noise mapping beyond these contours will improve the understanding of the perception, annoyance and health impact of aircraft operations. OpenSky data can provide the spatial data to create an aircraft noise exposure map for lower exposure levels. This work presents the first step of region-wide noise exposure methodology based on open source data: detecting low LAmax aircraft events in ambient noise using spectral noise measurements and

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Matsuoka, Daisuke, Fumiaki Araki, and Hideharu Sasaki. "Event detection and visualization of ocean eddies simulated by ocean general circulation model." International Journal of Modeling, Simulation, and Scientific Computing 10, no. 03 (2019): 1950018. http://dx.doi.org/10.1142/s1793962319500181.

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Numerical study of ocean eddies has been carried out by using high-resolution ocean general circulation models. In order to understand ocean eddies from the large volume data produced by simulations, visualizing only eddy distribution at each time step is insufficient; time-variations in eddy events and phenomena must also be considered. However, existing methods cannot precisely find and track eddy events such as amalgamation and bifurcation. In this study, we propose an original approach for eddy detection, tracking, and event visualization based on an eddy classification system. The propose

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Limbach, S., E. Schömer, and H. Wernli. "Detection, tracking and event localization of jet stream features in 4-D atmospheric data." Geoscientific Model Development 5, no. 2 (2012): 457–70. http://dx.doi.org/10.5194/gmd-5-457-2012.

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Abstract. We introduce a novel algorithm for the efficient detection and tracking of features in spatiotemporal atmospheric data, as well as for the precise localization of the occurring genesis, lysis, merging and splitting events. The algorithm works on data given on a four-dimensional structured grid. Feature selection and clustering are based on adjustable local and global criteria, feature tracking is predominantly based on spatial overlaps of the feature's full volumes. The resulting 3-D features and the identified correspondences between features of consecutive time steps are represente

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Limbach, S., E. Schömer, and H. Wernli. "Detection, tracking and event localization of interesting features in 4-D atmospheric data." Geoscientific Model Development Discussions 4, no. 4 (2011): 3013–45. http://dx.doi.org/10.5194/gmdd-4-3013-2011.

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Abstract. We introduce a novel algorithm for the efficient detection and tracking of interesting features in spatial-temporal atmospheric data, as well as for the precise localization of the occurring genesis, lysis, merging and splitting events. The algorithm is based on the well-known region growing segmentation method. We extended the basic idea towards the analysis of the complete 4-D dataset, identifying segments representing the spatial features and their development over time. Each segment consists of one set of distinct 3-D features per time step. The algorithm keeps track of the succe

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Poursoltanmohammadi, Amirsaman, and Matthew Sorell. "Reliable Motion Detection, Location and Audit in Surveillance Video." International Journal of Digital Crime and Forensics 1, no. 4 (2009): 19–31. http://dx.doi.org/10.4018/jdcf.2009062402.

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The review of video captured by fixed surveillance cameras is a time consuming, tedious, expensive and potentially unreliable human process, but of very high evidentiary value. Two key challenges stand out in such a task: 1.) ensuring that all motion events are captured for analysis, and 2.) demonstrating that all motion events have been captured so that the evidence survives being challenged in court. In previous work (Zhao, Poursoltanmohammadi & Sorell, 2008), it was demonstrated that tracking the average brightness of video frames or frame segment provided a more robust metric of motion

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Busler, J., H. Wehn, and L. Woodhouse. "TRACKING VESSELS TO ILLEGAL POLLUTANT DISCHARGES USING MULTISOURCE VESSEL INFORMATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 30, 2015): 927–32. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-927-2015.

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Illegal discharge of bilge waters is a significant source of oil and other environmental pollutants in Canadian and international waters. Imaging satellites are commonly used to monitor large areas to detect oily discharges from vessels, off-shore platforms and other sources. While remotely sensed imagery provides a snap-shot picture useful for detecting a spill or the presence of vessels in the vicinity, it is difficult to directly associate a vessel to an observed spill unless the vessel is observed while the discharge is occurring. The situation then becomes more challenging with increased

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Tang, Tao, and Guangmin Hu. "Detecting and Tracking Significant Events for Individuals on Twitter by Monitoring the Evolution of Twitter Followership Networks." Information 11, no. 9 (2020): 450. http://dx.doi.org/10.3390/info11090450.

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People publish tweets on Twitter to share everything from global news to their daily life. Abundant user-generated content makes Twitter become one of the major channels for people to obtain information about real-world events. Event detection techniques help to extract events from massive amounts of Twitter data. However, most existing techniques are based on Twitter information streams, which contain plenty of noise and polluted content that would affect the accuracy of the detecting result. In this article, we present an event discovery method based on the change of the user’s followers, wh

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Xu, Wei, Lingyu Liu, and Wei Shang. "Leveraging cross-media analytics to detect events and mine opinions for emergency management." Online Information Review 41, no. 4 (2017): 487–506. http://dx.doi.org/10.1108/oir-08-2015-0286.

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Purpose Timely detection of emergency events and effective tracking of corresponding public opinions are critical in emergency management. As media are immediate sources of information on emergencies, the purpose of this paper is to propose cross-media analytics to detect and track emergency events and provide decision support for government and emergency management departments. Design/methodology/approach In this paper, a novel emergency event detection and opinion mining method is proposed for emergency management using cross-media analytics. In the proposed approach, an event detection modu

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Dissertations / Theses on the topic "Events Detection and Tracking"

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Reverter, Valeiras David. "Event-based detection and tracking." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066566/document.

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L'objectif principal de cette thèse est le développement d'algorithmes événementiels pour la détection et le suivi d'objets. Ces algorithmes sont spécifiquement conçus pour travailler avec une sortie produite par des caméras neuromorphiques. Ce type de caméras sont un nouveau type de capteurs bio inspirés, dont le principe de fonctionnement s'inspire de la rétine: chaque pixel est indépendant et génère des événements de manière asynchrone lorsqu'un changement de luminosité suffisamment important est détecté à la position correspondante du plan focal. Cette nouvelle façon d'encoder l'informatio

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Abdelhaq, Hamed [Verfasser], and Michael [Akademischer Betreuer] Gertz. "Localized Events in Social Media Streams: Detection, Tracking, and Recommendation / Hamed Abdelhaq ; Betreuer: Michael Gertz." Heidelberg : Universitätsbibliothek Heidelberg, 2016. http://d-nb.info/118061075X/34.

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Hossain, Akdas, and Emma Miléus. "Eye Movement Event Detection for Wearable Eye Trackers." Thesis, Linköpings universitet, Matematik och tillämpad matematik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-129616.

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Eye tracking research is a growing area and the fields as where eye trackingcould be used in research are large. To understand the eye tracking data dif-ferent filters are used to classify the measured eye movements. To get accu-rate classification this thesis has investigated the possibility to measure bothhead movements and eye movements in order to improve the estimated gazepoint.The thesis investigates the difference in using head movement compensationwith a velocity based filter, I-VT filter, to using the same filter without headmovement compensation. Further on different velocity thresho

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Akman, Oytun. "Multi-camera Video Surveillance: Detection, Occlusion Handling, Tracking And Event Recognition." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608620/index.pdf.

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In this thesis, novel methods for background modeling, tracking, occlusion handling and event recognition via multi-camera configurations are presented. As the initial step, building blocks of typical single camera surveillance systems that are moving object detection, tracking and event recognition, are discussed and various widely accepted methods for these building blocks are tested to asses on their performance. Next, for the multi-camera surveillance systems, background modeling, occlusion handling, tracking and event recognition for two-camera configurations are examined. Various foregro

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Borovies, Drew A. "Particle filter based tracking in a detection sparse discrete event simulation environment." Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion.exe/07Mar%5FBorovies.pdf.

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Thesis (M.S. in Modeling, Virtual Environment, and Simulation (MOVES))--Naval Postgraduate School, March 2007.<br>Thesis Advisor(s): Christian Darken. "March 2007." Includes bibliographical references (p. 115). Also available in print.

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Adedoyin-Olowe, Mariam. "An association rule dynamics and classification approach to event detection and tracking in Twitter." Thesis, Robert Gordon University, 2015. http://hdl.handle.net/10059/1222.

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Twitter is a microblogging application used for sending and retrieving instant on-line messages of not more than 140 characters. There has been a surge in Twitter activities since its launch in 2006 as well as steady increase in event detection research on Twitter data (tweets) in recent years. With 284 million monthly active users Twitter has continued to grow both in size and activity. The network is rapidly changing the way global audience source for information and influence the process of journalism [Newman, 2009]. Twitter is now perceived as an information network in addition to being a

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Bartl, Vojtěch. "Mapování pohybu osob stacionární kamerou." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-235002.

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The aim of this diploma thesis is to obtain information on the motion of people in a scene from the record of the stationary camera. The procedure to detect exceptional events in the scene was designed. Exceptional events can be fast-moving persons, or persons moving in di erent places than everyone else in the scene. To trace the motion of persons, two algorithms were applied and tested - Optical flow and CAMSHIFT. The analysis of the resulting motions is performed by monitoring the progress of motion, and its comparison with the other motions in the scene. The analysis result is represented

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Oldham, Kevin M. "Table tennis event detection and classification." Thesis, Loughborough University, 2015. https://dspace.lboro.ac.uk/2134/19626.

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It is well understood that multiple video cameras and computer vision (CV) technology can be used in sport for match officiating, statistics and player performance analysis. A review of the literature reveals a number of existing solutions, both commercial and theoretical, within this domain. However, these solutions are expensive and often complex in their installation. The hypothesis for this research states that by considering only changes in ball motion, automatic event classification is achievable with low-cost monocular video recording devices, without the need for 3-dimensional (3D) pos

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Huh, Seungil. "Toward an Automated System for the Analysis of Cell Behavior| Cellular Event Detection and Cell Tracking in Time-lapse Live Cell Microscopy." Thesis, Carnegie Mellon University, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3538985.

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<p>Time-lapse live cell imaging has been increasingly employed by biological and biomedical researchers to understand the underlying mechanisms in cell physiology and development by investigating behavior of cells. This trend has led to a huge amount of image data, the analysis of which becomes a bottleneck in related research. Consequently, how to efficiently analyze the data is emerging as one of the major challenges in the fields. </p><p> Computer vision analysis of non-fluorescent microscopy images, representatively phase-contrast microscopy images, promises to realize a long-term monito

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Limbach, Sebastian [Verfasser]. "Software tools and efficient algorithms for the feature detection, feature tracking, event localization, and visualization of large sets of atmospheric data / Sebastian Limbach." Mainz : Universitätsbibliothek Mainz, 2013. http://d-nb.info/1041309724/34.

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Books on the topic "Events Detection and Tracking"

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Topic Detection and Tracking: Event-based Information Organization. Springer US, 2002.

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Allan, James, ed. Topic Detection and Tracking. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0933-2.

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Radar systems, peak detection and tracking. Newnes, 2002.

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Kolawole, Michael. Radar systems, peak detection and tracking. Butterworth-Heinemann, 2003.

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Gini, Fulvio. Knowledge based radar detection, tracking, and classification. Wiley, 2008.

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Gini, Fulvio. Knowledge based radar detection, tracking, and classification. Wiley, 2008.

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Okaie, Yutaka, Tadashi Nakano, Takahiro Hara, and Shojiro Nishio. Target Detection and Tracking by Bionanosensor Networks. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2468-9.

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Gini, Fulvio, and Muralidhar Rangaswamy, eds. Knowledge-Based Radar Detection, Tracking, and Classification. John Wiley & Sons, Inc., 2007. http://dx.doi.org/10.1002/9780470283158.

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Karasulu, Bahadir. Performance Evaluation Software: Moving Object Detection and Tracking in Videos. Springer New York, 2013.

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Shirman, Yakov D. Computer simulation of aerial target radar scattering, recognition, detection, and tracking. Artech House, 2002.

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Book chapters on the topic "Events Detection and Tracking"

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Feng, Guiliang, Yiping Lu, Jing Qin, and Xiao Zhang. "Social Events Detection and Tracking Based on Microblog." In Lecture Notes in Electrical Engineering. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0539-8_17.

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Andrienko, Natalia, Gennady Andrienko, Georg Fuchs, Salvatore Rinzivillo, and Hans-Dieter Betz. "Real Time Detection and Tracking of Spatial Event Clusters." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23461-8_38.

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Xu, Meng, Jiajun Cheng, Lixiang Guo, Pei Li, Xin Zhang, and Hui Wang. "TwiTracker: Detecting and Extracting Events from Twitter for Entity Tracking." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04503-6_11.

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Ríos, Antonio, Cristina Conde, Isaac Martín de Diego, and Enrique Cabello. "Driver’s Hand Detection and Tracking Based on Address Event Representation." In Computational Modeling of Objects Presented in Images. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04039-4_8.

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Long, Rui, Haofen Wang, Yuqiang Chen, Ou Jin, and Yong Yu. "Towards Effective Event Detection, Tracking and Summarization on Microblog Data." In Web-Age Information Management. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23535-1_55.

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Araujo, Lourdes, José A. Cuesta, and Juan J. Merelo. "Genetic Algorithm for Burst Detection and Activity Tracking in Event Streams." In Parallel Problem Solving from Nature - PPSN IX. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11844297_31.

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Lei, Zhen, Jianfeng Liao, Dong Li, and Lingda Wu. "Event Detection and Tracking Based on Improved Incremental K-Means and Transductive SVM." In Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85984-0_105.

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Liu, Xu, David Doermann, Huiping Li, K. C. Lee, Hasan Ozdemir, and Lipin Liu. "A Novel 2D Marker Design and Application for Object Tracking and Event Detection." In Advances in Visual Computing. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89639-5_24.

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Aktunc, Riza, Ismail Hakki Toroslu, and Pinar Karagoz. "Event Detection on Communities: Tracking the Change in Community Structure within Temporal Communication Networks." In Lecture Notes in Social Networks. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33698-1_5.

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Toomay, J. C. "Detection Tracking." In Radar Principles for the Non-Specialist. Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-011-6985-1_3.

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Conference papers on the topic "Events Detection and Tracking"

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Mamo, Nicholas, Joel Azzopardi, and Colin Layfield. "ELD: Event TimeLine Detection -- A Participant-Based Approach to Tracking Events." In HT '19: 30th ACM Conference on Hypertext and Social Media. ACM, 2019. http://dx.doi.org/10.1145/3342220.3344921.

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Tian Wang and H. Snoussi. "Histograms of optical flow orientation for abnormal events detection." In 2013 IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS 2013). IEEE, 2013. http://dx.doi.org/10.1109/pets.2013.6523794.

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Yu, Na, and Qi Han. "MEMS: Detection and tracking of mobile events using mobile sensors." In 2011 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON). IEEE, 2011. http://dx.doi.org/10.1109/sahcn.2011.5984891.

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Adavanne, Sharath, Archontis Politis, and Tuomas Virtanen. "Localization, Detection and Tracking of Multiple Moving Sound Sources with a Convolutional Recurrent Neural Network." In 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2019). New York University, 2019. http://dx.doi.org/10.33682/xb0q-a335.

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Hubbell, Nicholas, and Qi Han. "Detection and tracking of dynamic amorphous events in wireless sensor networks." In 2011 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON). IEEE, 2011. http://dx.doi.org/10.1109/sahcn.2011.5984894.

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Vairo, Claudio, Giuseppe Amato, Stefano Chessa, and Paolo Valleri. "Modeling detection and tracking of complex events in wireless sensor networks." In 2010 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2010. http://dx.doi.org/10.1109/icsmc.2010.5642242.

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El-Fallah, A., A. Zatezalo, R. Mahler, R. K. Mehra, and K. Pham. "Detection and tracking of LEO collision events using space-based sensors." In SPIE Defense, Security, and Sensing, edited by Khanh D. Pham, Henry Zmuda, Joseph Lee Cox, and Greg J. Meyer. SPIE, 2011. http://dx.doi.org/10.1117/12.884612.

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Macii, David, and Dario Petri. "Fast detection of rapid voltage change events through dynamic RMS voltage tracking." In 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2018. http://dx.doi.org/10.1109/i2mtc.2018.8409728.

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Cui, Peng, Li-Feng Sun, Zhi-Qiang Liu, and Shi-Qiang Yang. "A Sequential Monte Carlo Approach to Anomaly Detection in Tracking Visual Events." In 2007 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/cvpr.2007.383515.

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Yu, Na, and Qi Han. "Detection and tracking of mobile events with dynamic signatures using mobile sensors." In 2012 IEEE Consumer Communications and Networking Conference (CCNC). IEEE, 2012. http://dx.doi.org/10.1109/ccnc.2012.6180963.

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Reports on the topic "Events Detection and Tracking"

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Kashyap, Rangasami L. Multiple Target Detection and Tracking. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada363925.

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Cadzow, James A. Super-Resolution Target Detection and Tracking. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada307013.

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Elliott, G. R., and S. D. Stearns. Detection and frequency tracking of chirping signals. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/6451993.

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Czipott, Peter V., Alexander R. Perry, Brian R. Whitecotton, Yacine Dalichaouch, and David O. Walsh. Magnetic Detection and Tracking of Military Vehicles. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada409217.

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Nelson, Cynthia Lee. High resolution 3D insider detection and tracking. Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/875974.

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Gok, M. Rengin, Farra Al-Jerri, Douglas Dodge, Abdullah Al-Enezi, Terri Hauk, and R. Mellors. Detection of Local/Regional Events in Kuwait Using Next-Generation Detection Algorithms. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1252605.

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Bennett, Theron J., Brian W. Barker, Margaret E. Marshall, and John R. Murphy. Detection and Identification of Small Regional Seismic Events. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada305536.

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Li, Huiping, David Doermann, and Omid Kia. Automatic Text Detection and Tracking in Digital Video. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada458675.

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Lankford, Christopher P. Machine Detection of Operationally Significant Cognitive Events for C4ISR. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada418055.

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Florian, R., H. Hassan, A. Ittycheriah, et al. A Statistical Model for Multilingual Entity Detection and Tracking. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada460245.

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