Towards Efficient Human Action Retrieval based on Triplet-Loss Metric Learning
| Authors | |
|---|---|
| Year of publication | 2022 |
| Type | Article in Proceedings |
| Conference | 33rd International Conference on Database and Expert Systems Applications (DEXA) |
| MU Faculty or unit | |
| Citation | |
| Web | https://link.springer.com/chapter/10.1007/978-3-031-12423-5_18 |
| Doi | http://dx.doi.org/10.1007/978-3-031-12423-5_18 |
| Keywords | human motion data;skeleton sequences;action similarity;action retrieval;triplet-loss learning;LSTM |
| Description | Recent pose-estimation methods enable digitization of human motion by extracting 3D skeleton sequences from ordinary video recordings. Such spatio-temporal skeleton representation offers attractive possibilities for a wide range of applications but, at the same time, requires effective and efficient content-based access to make the extracted data reusable. In this paper, we focus on content-based retrieval of pre-segmented skeleton sequences of human actions to identify the most similar ones to a query action. We mainly deal with the extraction of content-preserving action features, which are learned using the triplet-loss approach in an unsupervised way. Such features are (1) effective as they achieve a similar retrieval quality as the features learned in a supervised way, and (2) of a fixed size which enables the application of indexing structures for efficient retrieval. |
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