DOI:10.1007/s11263-006-4329-6 Beyond Tracking: Modelling Activity and Understanding Behaviour
Abstract. In this work, we present a unified bottom-up and top-down automatic model selection based approach for modelling complex activities of multiple objects in cluttered scenes. An activity of multiple objects is represented based on discrete scene events and their behaviours are modelled by re...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.66.8445 2023-05-15T16:01:34+02:00 DOI:10.1007/s11263-006-4329-6 Beyond Tracking: Modelling Activity and Understanding Behaviour Tao Xiang Shaogang Gong The Pennsylvania State University CiteSeerX Archives 2005 application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.66.8445 http://www.dcs.qmw.ac.uk/~sgg/papers/XiangGongIJCV06-Beyond.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.66.8445 http://www.dcs.qmw.ac.uk/~sgg/papers/XiangGongIJCV06-Beyond.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://www.dcs.qmw.ac.uk/~sgg/papers/XiangGongIJCV06-Beyond.pdf dynamic scene modelling graph models discrete event recognition activity representation behaviour recognition dynamic probabilistic networks Bayesian model selection text 2005 ftciteseerx 2016-01-08T16:49:46Z Abstract. In this work, we present a unified bottom-up and top-down automatic model selection based approach for modelling complex activities of multiple objects in cluttered scenes. An activity of multiple objects is represented based on discrete scene events and their behaviours are modelled by reasoning about the temporal and causal correlations among different events. This is significantly different from the majority of the existing techniques that are centred on object tracking followed by trajectory matching. In our approach, object-independent events are detected and classified by unsupervised clustering using Expectation-Maximisation (EM) and classified using automatic model selection based on Schwarz’s Bayesian Information Criterion (BIC). Dynamic Probabilistic Networks (DPNs) are formulated for modelling the temporal and causal correlations among discrete events for robust and holistic scene-level behaviour interpretation. In particular, we developed a Dynamically Multi-Linked Hidden Markov Model (DML-HMM) based on the discovery of salient dynamic interlinks among multiple temporal processes corresponding to multiple event classes. A DML-HMM is built using BIC based factorisation resulting in its topology being intrinsically determined by the underlying causality and temporal order among events. Extensive experiments are conducted on modelling activities captured in different indoor and outdoor scenes. Our experimental results demonstrate that the performance of a DML-HMM on modelling group activities in a noisy and cluttered scene is superior compared to those of other comparable dynamic probabilistic networks including a Text DML Unknown |
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dynamic scene modelling graph models discrete event recognition activity representation behaviour recognition dynamic probabilistic networks Bayesian model selection |
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dynamic scene modelling graph models discrete event recognition activity representation behaviour recognition dynamic probabilistic networks Bayesian model selection Tao Xiang Shaogang Gong DOI:10.1007/s11263-006-4329-6 Beyond Tracking: Modelling Activity and Understanding Behaviour |
topic_facet |
dynamic scene modelling graph models discrete event recognition activity representation behaviour recognition dynamic probabilistic networks Bayesian model selection |
description |
Abstract. In this work, we present a unified bottom-up and top-down automatic model selection based approach for modelling complex activities of multiple objects in cluttered scenes. An activity of multiple objects is represented based on discrete scene events and their behaviours are modelled by reasoning about the temporal and causal correlations among different events. This is significantly different from the majority of the existing techniques that are centred on object tracking followed by trajectory matching. In our approach, object-independent events are detected and classified by unsupervised clustering using Expectation-Maximisation (EM) and classified using automatic model selection based on Schwarz’s Bayesian Information Criterion (BIC). Dynamic Probabilistic Networks (DPNs) are formulated for modelling the temporal and causal correlations among discrete events for robust and holistic scene-level behaviour interpretation. In particular, we developed a Dynamically Multi-Linked Hidden Markov Model (DML-HMM) based on the discovery of salient dynamic interlinks among multiple temporal processes corresponding to multiple event classes. A DML-HMM is built using BIC based factorisation resulting in its topology being intrinsically determined by the underlying causality and temporal order among events. Extensive experiments are conducted on modelling activities captured in different indoor and outdoor scenes. Our experimental results demonstrate that the performance of a DML-HMM on modelling group activities in a noisy and cluttered scene is superior compared to those of other comparable dynamic probabilistic networks including a |
author2 |
The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Tao Xiang Shaogang Gong |
author_facet |
Tao Xiang Shaogang Gong |
author_sort |
Tao Xiang |
title |
DOI:10.1007/s11263-006-4329-6 Beyond Tracking: Modelling Activity and Understanding Behaviour |
title_short |
DOI:10.1007/s11263-006-4329-6 Beyond Tracking: Modelling Activity and Understanding Behaviour |
title_full |
DOI:10.1007/s11263-006-4329-6 Beyond Tracking: Modelling Activity and Understanding Behaviour |
title_fullStr |
DOI:10.1007/s11263-006-4329-6 Beyond Tracking: Modelling Activity and Understanding Behaviour |
title_full_unstemmed |
DOI:10.1007/s11263-006-4329-6 Beyond Tracking: Modelling Activity and Understanding Behaviour |
title_sort |
doi:10.1007/s11263-006-4329-6 beyond tracking: modelling activity and understanding behaviour |
publishDate |
2005 |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.66.8445 http://www.dcs.qmw.ac.uk/~sgg/papers/XiangGongIJCV06-Beyond.pdf |
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DML |
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DML |
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http://www.dcs.qmw.ac.uk/~sgg/papers/XiangGongIJCV06-Beyond.pdf |
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.66.8445 http://www.dcs.qmw.ac.uk/~sgg/papers/XiangGongIJCV06-Beyond.pdf |
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