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Published Articles >> Table of Contents >> Abstract
30th Applied Imagery Pattern Recognition Workshop (AIPR'01)
p. 0119
Scene and Content Analysis from Multiple Video Streams
S. Guler, Northorp Grumman Information Technology / TASC
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/AIPR.2001.991213
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| Abstract |
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In this paper, we describe a framework for video
analysis and a method to detect and understand
the class of events we refer to as "split and merge
events" from single or multiple video streams. We
start with automatic detection of scene changes,
including camera operations such as zoom, pan,
tilts and scene cuts. For each new scene, camera
calibration is performed, the scene geometry is
estimated, to determine the absolute positions for
each detected object. Objects in the video scenes
are detected using an adaptive background
subtraction method and tracked over consecutive
frames. Objects are detected and tracked in a way
to identify the key split and merge behaviors where
one object splits into two or more objects and two
or more objects merge into one object. We have
identified split and merge behaviors as the key
behavior components for several higher level
activities such package drop-off, exchange
between people, people getting out of cars or
forming crowds etc. We embed the data about scenes,
camera parameters, object features, positions into the
video stream as metadata to correlate, compare and
associate the results for several related scenes and
achieve better video event understanding. This location
for the detailed syntactic information allows it to be
physically associated with the video itself and
guarantees that analysis results will be preserved while
in archival storage or when sub-clips are created for
distribution to other users. We present some
preliminary results over single and multiple video
streams.
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 Additional Information
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Citation:
S. Guler,
"Scene and Content Analysis from Multiple Video Streams,"
aipr,
p. 0119,
30th Applied Imagery Pattern Recognition Workshop (AIPR'01),
2001
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