Kinect depth video compression for action recognition
Fedorov, Igor
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https://hdl.handle.net/2142/49462
Description
Title
Kinect depth video compression for action recognition
Author(s)
Fedorov, Igor
Issue Date
2014-05-30T16:45:34Z
Director of Research (if dissertation) or Advisor (if thesis)
Moulin, Pierre
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2014-05-30T16:45:34Z
Keyword(s)
Kinect
Depth
Video
Compression
Action
Recognition
Abstract
Since the advent of the Kinect camera, depth videos have become easily accessible to consumers and researchers, allowing a variety of complex classification tasks to be done more accurately and easily than with RGB videos. The wide use of Kinect has created a need for effective compression algorithms. We present three compression schemes, all evaluated using a classification metric for human activity recognition. The first scheme uses the idea of companding to pre-process the data prior to compressing it with a standard H.264 coder. The second scheme uses a standard H.264 coder and appends additional feature bits to the compressed signal to aid in classification. The third compression scheme also uses a standard H.264 coder and attempts to improve classification performance by learning a mapping between features extracted from compressed videos and features extracted from uncompressed videos.
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