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On hierarchical modelling of motion for workflow analysis from overhead view

On hierarchical modelling of motion for workflow analysis from overhead view
On hierarchical modelling of motion for workflow analysis from overhead view
Understanding human behaviour is a high level perceptual problem, one which is often dominated by the contextual knowledge of the environment, and where concerns such as occlusion, scene clutter and high within-class variations are commonplace. Nonetheless, such understanding is highly desirable for automated visual surveillance.We consider this problem in a context of a workflow analysis within an industrial environment.The hierarchical nature of the workflow is exploited to split the problem into ‘activity’ and ‘task’ recognition. In this, sequences of low level activities are examined for instances of a task while the remainder are labelled as background. An initial prediction of activity is obtained using shape and motion based features of the moving blob of interest. A sequence of these activities is further adjusted by a probabilistic analysis of transitions between activities using hidden Markov models (HMMs). In task detection, HMMs are arranged to handle the activities within each task. Two separate HMMs for task and background compete for an incoming sequence of activities. Imagery derived from a camera mounted overhead the target scene has been chosen over the more conventional oblique views (from the side) as this view does not suffer from as much occlusion, and it poses a manageable detection and tracking problem while still retaining powerful cues as to the workflow patterns. We evaluate our approach both in activity and task detection on a challenging dataset of surveillance of human operators in a car manufacturing plant.The experimental results showthat our hierarchical approach can automatically segment the timeline and spatially localize a series of predefined tasks that are performed to complete a workflow.
0932-8092
Arbab-Zavar, Banafshe
40e175ea-6557-47c6-b759-318d7e24984b
Carter, John N.
e05be2f9-991d-4476-bb50-ae91606389da
Nixon, M.S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Arbab-Zavar, Banafshe
40e175ea-6557-47c6-b759-318d7e24984b
Carter, John N.
e05be2f9-991d-4476-bb50-ae91606389da
Nixon, M.S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Arbab-Zavar, Banafshe, Carter, John N. and Nixon, M.S. (2013) On hierarchical modelling of motion for workflow analysis from overhead view. Machine Vision and Applications. (doi:10.1007/s00138-013-0528-7).

Record type: Article

Abstract

Understanding human behaviour is a high level perceptual problem, one which is often dominated by the contextual knowledge of the environment, and where concerns such as occlusion, scene clutter and high within-class variations are commonplace. Nonetheless, such understanding is highly desirable for automated visual surveillance.We consider this problem in a context of a workflow analysis within an industrial environment.The hierarchical nature of the workflow is exploited to split the problem into ‘activity’ and ‘task’ recognition. In this, sequences of low level activities are examined for instances of a task while the remainder are labelled as background. An initial prediction of activity is obtained using shape and motion based features of the moving blob of interest. A sequence of these activities is further adjusted by a probabilistic analysis of transitions between activities using hidden Markov models (HMMs). In task detection, HMMs are arranged to handle the activities within each task. Two separate HMMs for task and background compete for an incoming sequence of activities. Imagery derived from a camera mounted overhead the target scene has been chosen over the more conventional oblique views (from the side) as this view does not suffer from as much occlusion, and it poses a manageable detection and tracking problem while still retaining powerful cues as to the workflow patterns. We evaluate our approach both in activity and task detection on a challenging dataset of surveillance of human operators in a car manufacturing plant.The experimental results showthat our hierarchical approach can automatically segment the timeline and spatially localize a series of predefined tasks that are performed to complete a workflow.

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Published date: August 2013
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 355670
URI: http://eprints.soton.ac.uk/id/eprint/355670
ISSN: 0932-8092
PURE UUID: 7919207d-59eb-4414-b25a-bda594aaec70
ORCID for M.S. Nixon: ORCID iD orcid.org/0000-0002-9174-5934

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Date deposited: 20 Aug 2013 15:33
Last modified: 15 Oct 2019 00:57

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