Article

Event Modeling and Recognition Using Markov Logic Networks

Authors:

Son D. Tran,

Larry S. DavisAuthors Info & Claims

ECCV '08: Proceedings of the 10th European Conference on Computer Vision: Part II

Pages 610 - 623

https://doi.org/10.1007/978-3-540-88688-4_45

Published: 12 October 2008 Publication History

Abstract

We address the problem of visual event recognition in surveillance where noise and missing observations are serious problems. Common sense domain knowledge is exploited to overcome them. The knowledge is represented as first-order logic production rules with associated weights to indicate their confidence. These rules are used in combination with a relaxed deduction algorithm to construct a network of grounded atoms, the Markov Logic Network. The network is used to perform probabilistic inference for input queries about events of interest. The system's performance is demonstrated on a number of videos from a parking lot domain that contains complex interactions of people and vehicles.

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Bhattacharjya DHassanzadeh OLuss RMurugesan KElkind E(2023)Probabilistic rule induction from event sequences with logical summary markov modelsProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/629(5667-5675)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/629
Venugopal DGogate V(2022)Evidence-Based Clustering for Scalable Inference in Markov LogicMachine Learning and Knowledge Discovery in Databases10.1007/978-3-662-44845-8_17(258-273)Online publication date: 10-Mar-2022
https://dl.acm.org/doi/10.1007/978-3-662-44845-8_17
Kirk NRamirez-Amaro KDean-Leon ESaveriano MCheng G(2021)Online prediction of activities with structure: Exploiting contextual associations and sequences2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids)10.1109/HUMANOIDS.2015.7363453(744-749)Online publication date: 9-Mar-2021
https://dl.acm.org/doi/10.1109/HUMANOIDS.2015.7363453
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Information

Published In

ECCV '08: Proceedings of the 10th European Conference on Computer Vision: Part II

October 2008

844 pages

ISBN:9783540886853

Editors:
David Forsyth
Computer Science Department, University of Illinois at Urbana Champaign, Urbana, USA IL 61801
,
Philip Torr
Department of Computing, Oxford Brookes University, Wheatley, Oxford, UK OX33 1HX
,
Andrew Zisserman
Department of Engineering Science, University of Oxford, Oxford, UK OX1 3PJ

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 12 October 2008

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Bhattacharjya DHassanzadeh OLuss RMurugesan KElkind E(2023)Probabilistic rule induction from event sequences with logical summary markov modelsProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/629(5667-5675)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/629
Venugopal DGogate V(2022)Evidence-Based Clustering for Scalable Inference in Markov LogicMachine Learning and Knowledge Discovery in Databases10.1007/978-3-662-44845-8_17(258-273)Online publication date: 10-Mar-2022
https://dl.acm.org/doi/10.1007/978-3-662-44845-8_17
Kirk NRamirez-Amaro KDean-Leon ESaveriano MCheng G(2021)Online prediction of activities with structure: Exploiting contextual associations and sequences2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids)10.1109/HUMANOIDS.2015.7363453(744-749)Online publication date: 9-Mar-2021
https://dl.acm.org/doi/10.1109/HUMANOIDS.2015.7363453
Li SWang LZhang RChang XLiu XXie YQi YSong LDaumé HSingh A(2020)Temporal logic point processesProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3525494(5990-6000)Online publication date: 13-Jul-2020
https://dl.acm.org/doi/10.5555/3524938.3525494
Yu TLi YLi B(2020)RhyRNN: Rhythmic RNN for Recognizing Events in Long and Complex VideosComputer Vision – ECCV 202010.1007/978-3-030-58607-2_8(127-144)Online publication date: 23-Aug-2020
https://dl.acm.org/doi/10.1007/978-3-030-58607-2_8
Belle V(2020)Logic, Probability and Action: A Situation Calculus PerspectiveScalable Uncertainty Management10.1007/978-3-030-58449-8_4(52-67)Online publication date: 23-Sep-2020
https://dl.acm.org/doi/10.1007/978-3-030-58449-8_4
Al Farabi KSarkhel SDey SVenugopal D(2019)Fine-Grained Explanations Using Markov LogicMachine Learning and Knowledge Discovery in Databases10.1007/978-3-030-46147-8_37(614-629)Online publication date: 16-Sep-2019
https://dl.acm.org/doi/10.1007/978-3-030-46147-8_37
Teslya NRyabchikov ILipkin E(2019)The Concept of the Deviant Behavior Detection System via Surveillance CamerasComputational Science and Its Applications – ICCSA 201910.1007/978-3-030-24311-1_12(169-183)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/978-3-030-24311-1_12
Sarkhel SVenugopal DRuozzi NGogate V(2017)Efficient inference for untied MLNsProceedings of the 26th International Joint Conference on Artificial Intelligence10.5555/3171837.3171933(4617-4624)Online publication date: 19-Aug-2017
https://dl.acm.org/doi/10.5555/3171837.3171933
Alevizos ESkarlatidis AArtikis APaliouras G(2017)Probabilistic Complex Event RecognitionACM Computing Surveys10.1145/311780950:5(1-31)Online publication date: 26-Sep-2017
https://dl.acm.org/doi/10.1145/3117809
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