A computer vision based camera pedestal's vertical motion control

Yi Da Xu; Joshua Brown; Jason Traish; Daniel Dezwa

doi:10.1109/ICPR.2008.4761732

A computer vision based camera pedestal's vertical motion control

Yi Da Xu, Joshua Brown, Jason Traish, Daniel Dezwa

Research output: Book chapter/Published conference paper › Conference paper › peer-review

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Abstract

Traditional camera pedestals are manually operated. Our long term goal is to construct a fully autonomous pedestal system which can respond to changes in a scene and mimicking the human camera operator. In this paper, we discuss our experiments to control the vertical motion of a pedestal by leveling its position with a human head or a tracked hand-held object. We describe a set of computer vision methods used in these experiments, including the head position tracking using Gaussian mixture model (GMM) of the foreground blob and hand-held object tracking using continuously adaptive mean shift (CAM-shift) with motion initialization. We also discuss the application of Kalman filter and showing its effect in the reduction of the number of jittering pedestal motions.

Original language	English
Title of host publication	IEEE International Conference on Pattern Recognition (ICPR)
Place of Publication	USA
Publisher	IEEE
Pages	1-4
Number of pages	4
DOIs	https://doi.org/10.1109/ICPR.2008.4761732
Publication status	Published - 2008
Event	ICPR 2008: 19th International Conference - Florida, USA, New Zealand Duration: 08 Dec 2008 → 11 Dec 2008

Conference

Conference	ICPR 2008: 19th International Conference
Country/Territory	New Zealand
Period	08/12/08 → 11/12/08

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10.1109/ICPR.2008.4761732

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title = "A computer vision based camera pedestal's vertical motion control",

abstract = "Traditional camera pedestals are manually operated. Our long term goal is to construct a fully autonomous pedestal system which can respond to changes in a scene and mimicking the human camera operator. In this paper, we discuss our experiments to control the vertical motion of a pedestal by leveling its position with a human head or a tracked hand-held object. We describe a set of computer vision methods used in these experiments, including the head position tracking using Gaussian mixture model (GMM) of the foreground blob and hand-held object tracking using continuously adaptive mean shift (CAM-shift) with motion initialization. We also discuss the application of Kalman filter and showing its effect in the reduction of the number of jittering pedestal motions.",

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note = "Imported on 03 May 2017 - DigiTool details were: publisher = USA: IEEE, 2008. Event dates (773o) = 8-11 Dec. 2008; Parent title (773t) = IEEE International Conference on Pattern Recognition (ICPR). ISSNs: 1051-4651; ; ICPR 2008: 19th International Conference ; Conference date: 08-12-2008 Through 11-12-2008",

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doi = "10.1109/ICPR.2008.4761732",

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AU - Brown, Joshua

AU - Traish, Jason

AU - Dezwa, Daniel

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PY - 2008

Y1 - 2008

N2 - Traditional camera pedestals are manually operated. Our long term goal is to construct a fully autonomous pedestal system which can respond to changes in a scene and mimicking the human camera operator. In this paper, we discuss our experiments to control the vertical motion of a pedestal by leveling its position with a human head or a tracked hand-held object. We describe a set of computer vision methods used in these experiments, including the head position tracking using Gaussian mixture model (GMM) of the foreground blob and hand-held object tracking using continuously adaptive mean shift (CAM-shift) with motion initialization. We also discuss the application of Kalman filter and showing its effect in the reduction of the number of jittering pedestal motions.

AB - Traditional camera pedestals are manually operated. Our long term goal is to construct a fully autonomous pedestal system which can respond to changes in a scene and mimicking the human camera operator. In this paper, we discuss our experiments to control the vertical motion of a pedestal by leveling its position with a human head or a tracked hand-held object. We describe a set of computer vision methods used in these experiments, including the head position tracking using Gaussian mixture model (GMM) of the foreground blob and hand-held object tracking using continuously adaptive mean shift (CAM-shift) with motion initialization. We also discuss the application of Kalman filter and showing its effect in the reduction of the number of jittering pedestal motions.

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DO - 10.1109/ICPR.2008.4761732

M3 - Conference paper

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PB - IEEE

CY - USA

T2 - ICPR 2008: 19th International Conference

Y2 - 8 December 2008 through 11 December 2008

ER -

A computer vision based camera pedestal's vertical motion control

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