Online classification of visual tasks for industrial workflow monitoring

Voulodimos, Athanasios; Kosmopoulos, Dimitrios; Veres, Galina; Gräbner, Helmut; Gool, Luc Van; Varvarigou, Theodora

doi:10.1016/j.neunet.2011.06.001

Cited by 28 publications

(30 citation statements)

References 32 publications

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“…We will outline the conditions which would need to be satisfied for this approach to be applicable to a new problem and provide guidelines for how to do so. In addition to what has been offered in [19,20] our approach enables us to (i) localize the tasks in image frame and (ii) offer a stage by stage commentary-like report of the task as it happens.…”

Section: Overview Of the Methodsmentioning

confidence: 99%

“…Manufacturing [7,9,19,20] and office [3,10,17], environments, medical operating rooms [12], TV studios [13] and smart homes for the elderly [8] are amongst the settings wherein 'action' perception and understanding from video streams had been studied. Pinhanez and Bobick [13] were among the first to analyse the actions within a video stream.…”

Section: Related Workmentioning

confidence: 99%

“…In this, all possible pair-wise spatial relations among objects are represented in a relational state space and a probabilistic Latent Semantic Analysis is used to model workflow from this relational state space. Voulodimos et al [20] and Oliver et al [11] used HMMs as a means for low level sensory data or high level decision fusion. Criticizing HMMs as requiring well-defined states and robust features, Veres et al [19] opt for a neural network: they used an echo state network to learn the patterns within an annotated global scene descriptor.…”

Section: Related Workmentioning

confidence: 99%

“…The other point of concern is the spatial segmentation of actions. The complex and cluttered environment which is analysed in this paper, poses an overwhelming detection and tracking problem as was experiences by [7,19,20]. They have thus opted for a global scene descriptor and abandoned the problem of spatial segmentation.…”

Section: Related Workmentioning

confidence: 99%

“…The dataset includes synchronized video captured from four oblique views of the same cell. Veres et al [19] and Voulodimos et al [20] have analysed the workflow patterns from these oblique views. The overhead view analysis of the workspace might not be the optimal view angle to observe individual activities which include motion perpendicular to this viewing plane, also an estimate of height is not acquirable.…”

Section: Workflow Patterns and The Datasetmentioning

confidence: 99%

See 4 more Smart Citations

On hierarchical modelling of motion for workflow analysis from overhead view

Arbab-Zavar

Carter

Nixon

2013

Machine Vision and Applications

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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 show that 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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Section: Overview Of the Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Workflow Patterns and The Datasetmentioning

confidence: 99%

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On hierarchical modelling of motion for workflow analysis from overhead view

Arbab-Zavar

Carter

Nixon

2013

Machine Vision and Applications

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CGVis: A Visualization-Based Learning Platform for Computational Geometry Algorithms

Voulodimos

Karagiannopoulos

Drosouli

et al. 2020

Lecture Notes in Computer Science

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A Method for Online Analysis of Structured Processes Using Bayesian Filters and Echo State Networks

Kosmopoulos

Makedon

2012

Computer Vision – ECCV 2012. Workshops and Demonstrations

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We propose a Bayesian filtering framework for online analysis of visual structured processes, which can be combined with the Echo State Network (ESN) to capture prior information. With the proposed method we mitigate the effective Markovian Behavior of the ESN. We are able to keep a set of hypotheses about the entire history of behaviors and to evaluate them online based on new observations. The performance is evaluated under two complex visual behavior understanding scenarios using public datasets: a visual process for a kitchen table preparation and a real life manufacturing process.

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Online classification of visual tasks for industrial workflow monitoring

Cited by 28 publications

References 32 publications

On hierarchical modelling of motion for workflow analysis from overhead view

On hierarchical modelling of motion for workflow analysis from overhead view

CGVis: A Visualization-Based Learning Platform for Computational Geometry Algorithms

A Method for Online Analysis of Structured Processes Using Bayesian Filters and Echo State Networks

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