Szczegóły publikacji
Opis bibliograficzny
CNN-based and DTW features for human activity recognition on depth maps / Jacek TRELIŃSKI, Bogdan KWOLEK // Neural Computing & Applications ; ISSN 0941-0643. — 2021 — vol. 33 iss. 21, s. 14551–14563. — Bibliogr. s. 14562–14563, Abstr. — Publikacja dostępna online od: 2021-05-12
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 137244 |
|---|---|
| Data dodania do BaDAP | 2021-11-03 |
| Tekst źródłowy | URL |
| DOI | 10.1007/s00521-021-06097-1 |
| Rok publikacji | 2021 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Neural Computing & Applications |
Abstract
In this work, we present a new algorithm for human action recognition on raw depth maps. At the beginning, for each class we train a separate one-against-all convolutional neural network (CNN) to extract class-specific features representing person shape. Each class-specific, multivariate time-series is processed by a Siamese multichannel 1D CNN or a multichannel 1D CNN to determine features representing actions. Afterwards, for the nonzero pixels representing the person shape in each depth map we calculate statistical features. On multivariate time-series of such features we determine Dynamic Time Warping (DTW) features. They are determined on the basis of DTW distances between all training time-series. Finally, each class-specific feature vector is concatenated with the DTW feature vector. For each action category we train a multiclass classifier, which predicts probability distribution of class labels. From pool of such classifiers we select a number of classifiers such that an ensemble built on them achieves the best classification accuracy. Action recognition is performed by a soft voting ensemble that averages distributions calculated by such classifiers with the largest discriminative power. We demonstrate experimentally that on MSR-Action3D and UTD-MHAD datasets the proposed algorithm attains promising results and outperforms several state-of-the-art depth-based algorithms.
