Augmenting Spatio-Temporal Human Motion Data for Effective 3D Action Recognition
| Authors | |
|---|---|
| Year of publication | 2019 |
| Type | Article in Proceedings |
| Conference | 21st IEEE International Symposium on Multimedia (ISM) |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1109/ISM46123.2019.00044 |
| Keywords | 3D skeleton sequence;multimedia data;data augmentation;action recognition;bidirectional LSTM |
| Description | Action recognition is a fundamental operation in 3D human motion analysis. Existing deep learning classifiers achieve a high recognition accuracy if large amounts of training data are provided. However, such data are difficult to obtain in a variety of application scenarios, mainly due to the high costs of motion capture technologies and an absence of suitable actors. In this paper, we propose augmentation techniques to artificially enlarge existing collections of 3D human skeleton sequences. The proposed techniques are especially useful for datasets distinguishing in a high number of classes, each of them characterized by only a limited number of action samples. We experimentally demonstrate that the augmented data help to significantly increase the recognition accuracy even using a standard deep learning architecture. |
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