Large-scale statistical modeling of motion patterns: A Bayesian nonparametric approach
We propose a novel framework for large-scale scene understanding in static camera surveillance. Our techniques combine fast rank-1 constrained robust PCA to compute the foreground, with non-parametric Bayesian models for inference. Clusters are extracted in foreground patterns using a joint multinom...
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Published: |
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/55762 |
| _version_ | 1848759700694237184 |
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| author | Rana, S. Phung, D. Pham, DucSon Venkatesh, S. |
| author_facet | Rana, S. Phung, D. Pham, DucSon Venkatesh, S. |
| author_sort | Rana, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | We propose a novel framework for large-scale scene understanding in static camera surveillance. Our techniques combine fast rank-1 constrained robust PCA to compute the foreground, with non-parametric Bayesian models for inference. Clusters are extracted in foreground patterns using a joint multinomial+Gaussian Dirichlet process model (DPM). Since the multinomial distribution is normalized, the Gaussian mixture distinguishes between similar spatial patterns but different activity levels (eg. car vs bike). We propose a modification of the decayed MCMC technique for incremental inference, providing the ability to discover theoretically unlimited patterns in unbounded video streams. A promising by-product of our framework is online, abnormal activity detection. A benchmark video and two surveillance videos, with the longest being 140 hours long are used in our experiments. The patterns discovered are as informative as existing scene understanding algorithms. However, unlike existing work, we achieve near real-time execution and encouraging performance in abnormal activity detection. © 2012 ACM. |
| first_indexed | 2025-11-14T10:04:03Z |
| format | Conference Paper |
| id | curtin-20.500.11937-55762 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:04:03Z |
| publishDate | 2012 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-557622017-11-02T07:17:49Z Large-scale statistical modeling of motion patterns: A Bayesian nonparametric approach Rana, S. Phung, D. Pham, DucSon Venkatesh, S. We propose a novel framework for large-scale scene understanding in static camera surveillance. Our techniques combine fast rank-1 constrained robust PCA to compute the foreground, with non-parametric Bayesian models for inference. Clusters are extracted in foreground patterns using a joint multinomial+Gaussian Dirichlet process model (DPM). Since the multinomial distribution is normalized, the Gaussian mixture distinguishes between similar spatial patterns but different activity levels (eg. car vs bike). We propose a modification of the decayed MCMC technique for incremental inference, providing the ability to discover theoretically unlimited patterns in unbounded video streams. A promising by-product of our framework is online, abnormal activity detection. A benchmark video and two surveillance videos, with the longest being 140 hours long are used in our experiments. The patterns discovered are as informative as existing scene understanding algorithms. However, unlike existing work, we achieve near real-time execution and encouraging performance in abnormal activity detection. © 2012 ACM. 2012 Conference Paper http://hdl.handle.net/20.500.11937/55762 10.1145/2425333.2425340 restricted |
| spellingShingle | Rana, S. Phung, D. Pham, DucSon Venkatesh, S. Large-scale statistical modeling of motion patterns: A Bayesian nonparametric approach |
| title | Large-scale statistical modeling of motion patterns: A Bayesian nonparametric approach |
| title_full | Large-scale statistical modeling of motion patterns: A Bayesian nonparametric approach |
| title_fullStr | Large-scale statistical modeling of motion patterns: A Bayesian nonparametric approach |
| title_full_unstemmed | Large-scale statistical modeling of motion patterns: A Bayesian nonparametric approach |
| title_short | Large-scale statistical modeling of motion patterns: A Bayesian nonparametric approach |
| title_sort | large-scale statistical modeling of motion patterns: a bayesian nonparametric approach |
| url | http://hdl.handle.net/20.500.11937/55762 |