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Title:Kinect depth video compression for action recognition
Author(s):Fedorov, Igor
Advisor(s):Moulin, Pierre
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(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.
Issue Date:2014-05-30
URI:http://hdl.handle.net/2142/49462
Rights Information:Copyright 2014 Igor Fedorov
Date Available in IDEALS:2014-05-30
Date Deposited:2014-05


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