Al Ghamdi, M. and Gotoh, Y. orcid.org/0000-0003-1668-0867 (2020) Graph-based topic models for trajectory clustering in crowd videos. Machine Vision and Applications, 31. 39. ISSN 0932-8092
Abstract
Probabilistic topic modelings, such as latent Dirichlet allocation (LDA) and correlated topic models (CTM), have recently emerged as powerful statistical tools for processing video content. They share an important property, i.e., using a common set of topics to model all data. However such property can be too restrictive for modeling complex visual data such as crowd scenes where multiple fields of heterogeneous data jointly provide rich information about objects and events. This paper proposes graph-based extensions of LDA and CTM, referred to as GLDA and GCTM, to learn and analyze motion patterns by trajectory clustering in a highly cluttered and crowded environment. Unlike previous works that relied on a scene prior, we apply a spatio-temporal graph (STG) to uncover the spatial and temporal coherence between the trajectories of crowd motion during the learning process. The presented models advance the conventional approaches by integrating a manifold-based clustering as initialization and iterative statistical inference as optimization. The output of GLDA and GCTM are mid-level features that represent the motion patterns used later to generate trajectory clusters. Experiments on three different datasets show the effectiveness of the approaches in trajectory clustering and crowd motion modeling.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Springer Nature. This is an author-produced version of a paper subsequently published in Machine Vision and Applications. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | clustering; crowd videos; graph; manifold; embedding; topic modeling |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 15 Jun 2020 07:38 |
Last Modified: | 12 Jun 2021 00:38 |
Status: | Published |
Publisher: | Springer |
Refereed: | Yes |
Identification Number: | 10.1007/s00138-020-01092-3 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:161769 |