Indexing of motion capture data for efficient and fast similarity search

As motion capture systems are increasingly used for motion tracking and capture, and more and more surveillance cameras are installed for security protection, more and more motion data, including 3D motion data becomes available, making it important to index motion data for quick retrieval of similar motions. This paper proposes a simple and efficient tree structure for indexing motion data with dozens of attributes. Feature vectors are extracted for indexing by using singular value decomposition (SVD) properties of motion data matrices. By having similar motions with large variations indexed together, searching for similar motions of a query needs only one node traversal at each tree level, and only one feature needs to be considered at one tree level. Experiments with real hand gestures, arm motions and full body motions show that the majority of irrelevant motions can be pruned while retrieving all similar motions, and the traversal of an indexing tree for a query takes only a few microseconds.