Szczegóły publikacji
Opis bibliograficzny
Ensemble of classifiers using CNN and hand-crafted features for depth-based action recognition / Jacek TRELIŃSKI, Bogdan KWOLEK // W: Artificial Intelligence and Soft Computing : 18th International conference, ICAISC 2019 : Zakopane, Poland, June 16-20 2019 : proceedings, Pt. 2 / eds. Leszek Rutkowski, Rafał Scherer, Marcin Korytkowski, Witold Pedrycz, Ryszard Tadeusiewicz, Jacek M. Zurada. — Cham : Springer, cop. 2019. — (Lecture Notes in Computer Science ; ISSN 0302-9743. Lecture Notes in Artificial Intelligence ; LNAI 11509). — ISBN: 978-3-030-20914-8; e-ISBN: 978-3-030-20915-5. — S. 91-103. — Bibliogr. s. 101-103, Abstr. — Publikacja dostępna online od: 2019-05-27
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 128070 |
|---|---|
| Data dodania do BaDAP | 2020-03-20 |
| Tekst źródłowy | URL |
| DOI | 10.1007/978-3-030-20915-5_9 |
| Rok publikacji | 2019 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Artificial Intelligence and Soft Computing 2019 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
In this paper, we present an algorithm for action recognition that uses only depth maps. At the beginning we extract features describing the person shape in single depth maps. For each class we train a separate one-against-all convolutional neural network to extract class-specific features. The actions are represented by multivariate time-series of such CNN-based frame features for which we calculate statistical features. For the non-zero pixels representing the person shape in each depth map we calculate handcrafted features. For time-series of such handcrafted features we calculate the statistical features. Afterwards, handcrafted features that are common for all actions and CNN-based features that are action-specific are concatenated together resulting in action feature vectors. For each action feature vector we train a multi-class classifier with one-hot encoding of output labels. The prediction of the action is done by a voting-based ensemble operating on such one-hot encoding outputs. We demonstrate experimentally that on UTD-MHAD dataset the proposed algorithm outperforms state-of-the-art depth-based algorithms and achieves promising results on MSR-Action3D dataset. © Springer Nature Switzerland AG 2019.
