Optimising dynamic graphical models for video content analysis

Xiang, Tao; Gong, Shaogang

doi:10.1016/j.cviu.2008.05.011

Cited by 7 publications

(2 citation statements)

References 34 publications

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“…The use of model constraints to recognize complex events has been suggested before in works like [20,11,18,17]. Xiang et al [20] address structure learning in HMMs to obtain temporal dependencies between a few high-level events for video segmentation.…”

Section: Related Workmentioning

confidence: 99%

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Workflow monitoring based on 3D motion features

Padoy

Mateus

Weinland

et al. 2009

2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops

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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.

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Section: Related Workmentioning

confidence: 99%

“…Xiang et al [20] address structure learning in HMMs to obtain temporal dependencies between a few high-level events for video segmentation. An HMM models the simultaneous output of event-classifiers to filter their wrong detections.…”

Section: Related Workmentioning

confidence: 99%