{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/49462"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/49462","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Kinect depth video compression for 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.","abstract_html":"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.","abstract_has_math":false,"creators":["Fedorov, Igor"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Moulin, Pierre"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-05-30T16:45:34Z","date_published":"2014-05-30T16:45:34Z","updated_at":"2026-07-22T22:25:38Z","subjects":["Kinect","Depth","Video","Compression","Action","Recognition"],"languages":["en"],"rights":["Copyright 2014 Igor Fedorov"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/49462","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Moulin, Pierre"]},{"key":"dc:creator","label":"Author","values":["Fedorov, Igor"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-05-30T16:45:34Z","2014-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Kinect","Depth","Video","Compression","Action","Recognition"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2014 Igor Fedorov"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/49462"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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. 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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.","Item withdrawn by Laura Spradlin (lspradl2@illinois.edu) on 2014-04-29T14:00:26Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 Fedorov_Igor.tex: 93066 bytes, checksum: 118c16543d8a3338d4e8f22af6dcd70b (MD5) Fedorov_Igor.pdf: 2021342 bytes, checksum: f47d8575a788e59b6fc8301a074c2f95 (MD5)","Made available in DSpace on 2014-05-30T16:45:34Z (GMT). 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