Towards Scalable Retrieval of Human Motion Episodes
| Authors | |
|---|---|
| Year of publication | 2020 |
| Type | Article in Proceedings |
| Conference | 22nd IEEE International Symposium on Multimedia (ISM) |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1109/ISM.2020.00015 |
| Keywords | skeleton data; human motion retrieval; motion episodes; text-based processing |
| Description | With the increasing availability of human motion data captured in the form of 2D/3D skeleton sequences, more complex motion recordings need to be processed. In this paper, we study the problem of similarity-based matching of medium-sized unsegmented skeleton sequences, which we denote as motion episodes. We first apply standard pose-based approaches for matching episodes and analyze their shortcomings. Then, we adopt a recent segment-based approach that transforms episode data into a text-like representation, and apply mature text-processing techniques for matching episodes. We demonstrate that this text-based approach achieves promising results in the terms of both effectiveness and efficiency, and can be further indexed to implement scalable episode retrieval. |
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