A Key-Pose Similarity Algorithm for Motion Data Retrieval

  • Jan Sedmidubsky
  • Jakub Valcik
  • Pavel Zezula
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8192)


Analysis of human motion data is an important task in many research fields such as sports, medicine, security, and computer animation. In order to fully exploit motion databases for further processing, effective and efficient retrieval methods are needed. However, such task is difficult primarily due to complex spatio-temporal variances of individual human motions and the rapidly increasing volume of motion data. In this paper, we propose a universal content-based subsequence retrieval algorithm for indexing and searching motion data. The algorithm is able to examine database motions and locate all their sub-motions that are similar to a query motion example. We illustrate the algorithm usability by indexing motion features in form of joint-angle rotations extracted from a real-life 68-minute human motion database. We analyse the algorithm time complexity and evaluate retrieval effectiveness by comparing the search results against user-defined ground truth. The algorithm is also incorporated in an online web application facilitating query definition and visualization of search results.


Motion Data Motion Feature Range Query Dynamic Time Warping Retrieval Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jan Sedmidubsky
    • 1
  • Jakub Valcik
    • 1
  • Pavel Zezula
    • 1
  1. 1.Masaryk UniversityBrnoCzech Republic

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