Kinect depth video compression for action recognition
Fedorov, Igor
Loading…
Permalink
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
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.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
