@inproceedings{3669,
  keywords = {Analysis and modelling, Computer Vision, Contemporary musics, High level semantics, Historic preservation, Intangible cultural heritage, Intangible cultural heritages, Knowledge management, Knowledge transfer, Learning procedures, Multisensory, Multisensory Capturing and Analysis, Semantic Media Interpretation, Semantics, Sensorimotor learning, Sustainable development, Technology transfer, Transmission and Preservation},
  author = {Kosmas Dimitropoulos and Sotiris Manitsaris and Filareti Tsalakanidou and Spiros Nikolopoulos and Bruce Denby and Samer Al Kork and Lise Crevier-Buchman and Claire Pillot-Loiseau and Martine Adda-Decker and Stephane Dupont and Joelle Tilmanne and Michela Ott and Marilena Alivizatou and Erdal Yilmaz and Leontios Hadjileontiadis and Vassilios Charisis and Olivier Deroo and Athanasios Manitsaris and Ioannis Kompatsiaris and Nikos Grammalidis},
  title = {Capturing the intangible: An introduction to the i-treasures project},
  abstract = {Ports are a key part of cultural identity, and it is necessary to preserve them as important intangible Cultural Heritage, especially the human motion techniques specific to individual sports. In this paper we present a method for extracting 3D athlete motion from video broadcast sources, providing an important tool for preserving the heritage represented by these movements. Broadcast videos include camera motion, multiple player interaction, occlusions and noise, presenting significant challenges to solve the reconstruction. The approach requires initial definition of some key-frames and setting of 2D keypoints in those frames manually. Thereafter an automatic process estimates the poses and positions of the players in the key-frames, and in the frames between key-frames, taking into account collisions with the environment and human kinematic constraints. Initial results are extremely promising and this data could be used to analyze the sport s evolution over time, or even to generate animations for interactive applications.},
  year = {2014},
  booktitle = {VISAPP - Proc. Int. Conf. Comput. Vis. Theory Appl.},
  series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  volume = {2},
  pages = {773-781},
  publisher = {Springer Verlag},
  school = {Springer Verlag},
  address = {Lisbon},
  isbn = {03029743 (ISSN)},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84911883403&doi=10.1007%2f978-3-319-13695-0&partnerID=40&md5=7c8e92b649c01dedbc1a75ba128f1c80},
  doi = {10.1007/978-3-319-13695-0},
  note = {Journal Abbreviation: Lect. Notes Comput. Sci. Pages: 58 Publication Title: Lect. Notes Comput. Sci.},
  language = {English},
}