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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Padoy, N. Mateus, D. Weinland, D. Berger, M.-O. Navab, N. |
| Copyright Year | 2009 |
| Description | Author affiliation: TUM, Munich, Germany (Padoy, N.; Mateus, D.; Navab, N.) || INRIA, Nancy, France (Berger, M.-O.) || EPFL, Lausanne, Switzerland (Weinland, D.) |
| Abstract | Activity recognition has primarily addressed the identification of either actions or well-defined interactions among objects in a scene. In this work, we extend the scope to the study of workflow monitoring. In a workflow, ordered groups of activities (phases) with different durations take place in a constrained environment and create temporal patterns across the workflow instances. We address the problem of recognizing phases, based on exemplary recordings. We propose to use Workflow-HMMs, a form of HMMs augmented with phase probability variables that model the complete workflow process. This model takes into account the full temporal context which improves on-line recognition of the phases, especially in case of partial labeling. Targeted applications are workflow monitoring in hospitals and factories, where common action recognition approaches are difficult to apply. To avoid interfering with the normal workflow, we capture the activity of a room with a multiple-camera system. Additionally, we propose to rely on real-time low-level features (3D motion flow) to maintain a generic approach. We demonstrate our methods on sequences from medical procedures performed in a mock-up operating room. The sequences follow a complex workflow, containing various alternatives. |
| Starting Page | 585 |
| Ending Page | 592 |
| File Size | 1054920 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424444427 |
| DOI | 10.1109/ICCVW.2009.5457648 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-09-27 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Computer vision Hospitals Layout Hidden Markov models Humans Surgery Production facilities Surges Monitoring Videos |
| Content Type | Text |
| Resource Type | Article |
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