VALČÍK, Jakub, Jan SEDMIDUBSKÝ and Pavel ZEZULA. Assessing similarity models for human-motion retrieval applications. Computer Animation and Virtual Worlds, John Wiley & Sons Ltd, 2016, vol. 27, No 5, p. 484-500. ISSN 1546-4261. doi:10.1002/cav.1674.
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Basic information
Original name Assessing similarity models for human-motion retrieval applications
Authors VALČÍK, Jakub (203 Czech Republic, belonging to the institution), Jan SEDMIDUBSKÝ (203 Czech Republic, guarantor, belonging to the institution) and Pavel ZEZULA (203 Czech Republic, belonging to the institution).
Edition Computer Animation and Virtual Worlds, John Wiley & Sons Ltd, 2016, 1546-4261.
Other information
Original language English
Type of outcome article in a journal
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
Impact factor Impact factor: 0.424
RIV identification code RIV/00216224:14330/16:00087724
Organization unit Faculty of Informatics
UT WoS 000385614100003
Keywords in English human-motion retrieval; similarity model; effectiveness evaluation; motion capture data; action recognition
Tags best, DISA
Tags International impact, Reviewed
Changed by Changed by: RNDr. Jan Sedmidubský, Ph.D., učo 60474. Changed: 16/4/2019 07:36.
The development of motion capturing devices poses new challenges in the exploitation of human-motion data for various application fields, such as computer animation, visual surveillance, sports or physical medicine. Recently, a number of approaches dealing with motion data have been proposed, suggesting characteristic motion features to be extracted and compared on the basis of similarity. Unfortunately, almost each approach defines its own set of motion features and comparison methods, thus it is hard to fairly decide which similarity model is the most suitable for a given kind of human-motion retrieval application. To cope with this problem, we propose the HumAn Motion Model EvaluatoR (HAMMER) which is a generic framework for assessing candidate similarity models with respect to the purpose of the target application. The application purpose is specified by a user in form of a representative sample of categorized motion data. Respecting such categorization, the similarity models are assessed from the effectiveness and efficiency points of view using a set of space-complexity, information-retrieval, and performance measures. The usability of the framework is demonstrated by case studies of three practical examples of retrieval applications focusing on recognition of actions, detection of similar events, and identification of subjects.
GBP103/12/G084, research and development projectName: Centrum pro multi-modální interpretaci dat velkého rozsahu
Investor: Czech Science Foundation, Projects to promote excellence in basic research
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