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https://doi.org/10.1109/percom...
Article . 2004 . Peer-reviewed
Data sources: Crossref
DBLP
Conference object . 2023
Data sources: DBLP
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Augmenting film and video footage with sensor data

Authors: Norman Makoto Su; Heemin Park; Eric Bostrom; Jeff Burke; Mani B. Srivastava; Deborah Estrin;

Augmenting film and video footage with sensor data

Abstract

With the advent of tiny networked devices, Mark Weiser's vision of a world embedded with invisible computers is coming to age. Due to their small size and relative ease of deployment, sensor networks have been utilized by zoologists, seismologists and military personnel. In this paper, we investigate the application of sensor networks to the film industry. In particular, we are interested in augmenting film and video footage with sensor data. Unobtrusive sensors are deployed on a film set or in a television studio and on performers. During a filming of a scene, sensor data such as light intensity, color temperature and location are collected and synchronized with each film or video frame. Later, editors, graphics artists and programmers can view this data in synchronization with film and video playback. For example, such data can help define a new level of seamless integration between computer graphics and real world photography. A real-time version of our system would allow sensor data to trigger camera movement and cue special effects. In this paper, we discuss the design and implementation of the first part of our embedded film set environment, the augmented recording system. Augmented recording is a foundational component for the UCLA Hypermedia Studio's research into the use of sensor networks in film and video production. In addition, we have evaluated our system in a television studio.

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United States
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    popularity
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    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Average
Top 10%
Average
Green