Efficient Retrieval of Human Motion Episodes Based on Indexed Motion-Word Representations

Abstract

With the increasing availability of human motion data captured in the form of 2D or 3D skeleton sequences, more complex motion recordings need to be processed. In this paper, we focus on similarity-based indexing and efficient retrieval of motion episodes — medium-sized skeleton sequences that consist of multiple semantic actions and correspond to some logical motion unit (e.g. a figure skating performance). As a first step toward efficient retrieval, we apply the motion-word technique to transform spatio-temporal skeleton sequences into compact text-like documents. Based on these documents, we introduce a two-phase retrieval scheme that first finds a set of candidate query results and then re-ranks these candidates with more expensive application-specific methods. We further index the motion-word documents using inverted files, which allows us to retrieve the candidate documents in an efficient and scalable manner. We also propose additional query-reduction techniques that accelerate both the retrieval phases by removing semantically irrelevant parts of the motion query. Experimental evaluation is used to analyze the effects of the individual proposed techniques on the retrieval efficiency and effectiveness.