Searching Human Actions based on a Multidimensional Time Series Similarity Calculation Method

With the rapid performance improvement and popularization of sensor devices, a large amount of human action data can be captured in databases. Classification, recognition, searching, and mining of such human actions are promising applications. Although many of these applications have been developed, searching the large quantity of data, especially given the high dimensionality of the captured temporal data sequence is time-consuming. To reduce this time cost, we use a novel method for approximating a multi-dimensional time-series, named multidimensional time-series Approximation with use of Local features at Thinned-out Keypoints (A-LTK). With A-LTK applications for two human motion types, sign language and dancing, we found that the categorization of human action data and the search for the most similar human action became more accurate and reduced the time cost.