Temporal-Based Action Clustering for Motion Tendencies
Video-based action recognition encompasses the recognition of appearance and the classification of action types. This work proposes a discrete-temporal-sequence-based motion tendency clustering framework to implement motion clustering by extracting motion tendencies and self-supervised learning. A p...
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| Veröffentlicht in: | IEICE Transactions on Information and Systems 2023/08/01, Vol.E106.D(8), pp.1292-1295 |
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| container_title | IEICE Transactions on Information and Systems |
| container_volume | E106.D |
| creator | QIAN, Xingyu CHEN, Xiaogang YUEMAIER, Aximu LI, Shunfen DAI, Weibang SONG, Zhitang |
| description | Video-based action recognition encompasses the recognition of appearance and the classification of action types. This work proposes a discrete-temporal-sequence-based motion tendency clustering framework to implement motion clustering by extracting motion tendencies and self-supervised learning. A published traffic intersection dataset (inD) and a self-produced gesture video set are used for evaluation and to validate the motion tendency action recognition hypothesis. |
| doi_str_mv | 10.1587/transinf.2023EDL8001 |
| format | Article |
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| ispartof | IEICE Transactions on Information and Systems, 2023/08/01, Vol.E106.D(8), pp.1292-1295 |
| issn | 0916-8532 1745-1361 |
| language | eng |
| recordid | cdi_proquest_journals_2864814987 |
| source | J-STAGE Free; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | action clustering Activity recognition Clustering continuous learning Machine learning motion-tendency Self-supervised learning self-supervision |
| title | Temporal-Based Action Clustering for Motion Tendencies |
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