AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition
Activity recognition is an important issue in building intelligent monitoring systems. We address the recognition of multilevel activities in this paper via a conditional Markov random field (MRF), known as the dynamic conditional random field (DCRF). Parameter estimation in general MRFs using maxim...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE Computer Society Conference Publishing Services
2006
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| Online Access: | http://hdl.handle.net/20.500.11937/16060 |
| _version_ | 1848749064978432000 |
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| author | Truyen, Tran Phung, Dinh Bui, H. Venkatesh, Svetha |
| author2 | A. Fitzgibbon |
| author_facet | A. Fitzgibbon Truyen, Tran Phung, Dinh Bui, H. Venkatesh, Svetha |
| author_sort | Truyen, Tran |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Activity recognition is an important issue in building intelligent monitoring systems. We address the recognition of multilevel activities in this paper via a conditional Markov random field (MRF), known as the dynamic conditional random field (DCRF). Parameter estimation in general MRFs using maximum likelihood is known to be computationally challenging (except for extreme cases), and thus we propose an efficient boosting-based algorithm AdaBoost.MRF for this task. Distinct from most existing work, our algorithm can handle hidden variables (missing labels) and is particularly attractive for smarthouse domains where reliable labels are often sparsely observed. Furthermore, our method works exclusively on trees and thus is guaranteed to converge. We apply the AdaBoost.MRF algorithmto a home video surveillance application and demonstrate its efficacy. |
| first_indexed | 2025-11-14T07:15:00Z |
| format | Conference Paper |
| id | curtin-20.500.11937-16060 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:15:00Z |
| publishDate | 2006 |
| publisher | IEEE Computer Society Conference Publishing Services |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-160602022-10-27T07:51:49Z AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition Truyen, Tran Phung, Dinh Bui, H. Venkatesh, Svetha A. Fitzgibbon C. Taylor Y. LeCun Activity recognition is an important issue in building intelligent monitoring systems. We address the recognition of multilevel activities in this paper via a conditional Markov random field (MRF), known as the dynamic conditional random field (DCRF). Parameter estimation in general MRFs using maximum likelihood is known to be computationally challenging (except for extreme cases), and thus we propose an efficient boosting-based algorithm AdaBoost.MRF for this task. Distinct from most existing work, our algorithm can handle hidden variables (missing labels) and is particularly attractive for smarthouse domains where reliable labels are often sparsely observed. Furthermore, our method works exclusively on trees and thus is guaranteed to converge. We apply the AdaBoost.MRF algorithmto a home video surveillance application and demonstrate its efficacy. 2006 Conference Paper http://hdl.handle.net/20.500.11937/16060 10.1109/CVPR.2006.49 IEEE Computer Society Conference Publishing Services restricted |
| spellingShingle | Truyen, Tran Phung, Dinh Bui, H. Venkatesh, Svetha AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition |
| title | AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition |
| title_full | AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition |
| title_fullStr | AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition |
| title_full_unstemmed | AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition |
| title_short | AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition |
| title_sort | adaboost.mrf: boosted markov random forests and application to multilevel activity recognition |
| url | http://hdl.handle.net/20.500.11937/16060 |