Indexing and Retrieval of Human Motion Data Based on a Growing Self-Organizing Map

  • 余 大成

Student thesis: Master's Thesis

Abstract

With low-cost depth cameras are released recently motion data containing 3D coordinates of skeleton joints during a time period can be directly captured Nevertheless analyzing the motion data is usually a challenging problem and requires huge computation costs because of the high-dimensionality Among several alternatives the self-organizing map (SOM) is verified to be an effective technique to handle such motion data Specifically a captured motion sequence can be easily and precisely mapped to form an indexed motion string through the use of a trained SOM However the training process of the SOM is high computation complexity and is thus typically tedious In view of this we propose in this work to incorporate a hierarchical structure into the SOM technique Generally speaking our approach named as GQSOM (growing quadtree self-organizing map) helps to significantly reduce the required computation complexity while preserving the effectiveness of the SOM technique Empirical studies using the WorkoutSU-10 exercise dataset show that our approach is both efficient and effective to perform indexing and retrieval tasks of motion data
Date of Award2014 Aug 9
Original languageEnglish
SupervisorWei-Guang Teng (Supervisor)

Cite this

Indexing and Retrieval of Human Motion Data Based on a Growing Self-Organizing Map
大成, 余. (Author). 2014 Aug 9

Student thesis: Master's Thesis