TY - CPAPER
KW - Digital storage
KW - Extraction
KW - Intangible cultural heritages
KW - K-means
KW - Motion capturing
KW - Musculoskeletal system
KW - Serious games
KW - Spatio-temporal dependencies
KW - Storage complexity
KW - Unsupervised clustering
KW - Virtual reality
KW - choreogrpahy summarization
KW - clustering
KW - k-means
KW - key posture extraction
KW - Motion capturing
AU - Ioannis Rallis
AU - Ioannis Georgoulas
AU - Nikolaos Doulamis
AU - Athanasios Voulodimos
AU - Panagiotis Terzopoulos
AB - Modelling and digitizing performing arts through motion capturing interfaces is an important aspect for the analysis, processing and documentation of intangible cultural heritage assets. However, existing modelling approaches may involve huge amounts of information which are difficult to process, store and analyze. To address these limitations, usually a skeleton describing the dancer motion is extracted. However, often the complexity still remains due to the high spatio-temporal dependencies of the detected skeleton joints. In this paper, an alternative approach is presented: choreography summarization. This means that a very small number of image frames are extracted to represent a choreography, thus significantly reducing the processing and storage complexity. In our approach the problem of choreography summarization is treated as an unsupervised clustering approach. Evaluation indices are introduced for monitoring the summarization performance. Experimental results on real-life dancing performances verifies the capability of the proposed method to capture the main patterns of a choreography with a very small number of trajectory points.
C2 - Int. Conf. Virtual Worlds Games Serious Appl., VS-Games - Proc.
DO - 10.1109/VS-GAMES.2017.8056576
N1 - Journal Abbreviation: Int. Conf. Virtual Worlds Games Serious Appl., VS-Games - Proc.
N2 - Modelling and digitizing performing arts through motion capturing interfaces is an important aspect for the analysis, processing and documentation of intangible cultural heritage assets. However, existing modelling approaches may involve huge amounts of information which are difficult to process, store and analyze. To address these limitations, usually a skeleton describing the dancer motion is extracted. However, often the complexity still remains due to the high spatio-temporal dependencies of the detected skeleton joints. In this paper, an alternative approach is presented: choreography summarization. This means that a very small number of image frames are extracted to represent a choreography, thus significantly reducing the processing and storage complexity. In our approach the problem of choreography summarization is treated as an unsupervised clustering approach. Evaluation indices are introduced for monitoring the summarization performance. Experimental results on real-life dancing performances verifies the capability of the proposed method to capture the main patterns of a choreography with a very small number of trajectory points.
PB - Institute of Electrical and Electronics Engineers Inc.
SN - 9781509058129 (ISBN)
SP - 94
EP - 101
TI - Extraction of key postures from 3D human motion data for choreography summarization
UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034659414&doi=10.1109%2fVS-GAMES.2017.8056576&partnerID=40&md5=3259f814481d16dfa64f01e91ed52301
ER -