RT Conference Proceedings
T1 Skeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networks
A1 Hieu Pham, Huy
A1 Khoudour, Louahdi
A1 Crouzil, Alain
A1 Zegers, Pablo
A1 Velastin Carroza, Sergio Alejandro
AB We propose a novel skeleton-based representation for 3D action recognition in videos using Deep Convolutional Neural Networks (D-CNNs). Two key issues have been addressed: First, how to construct a robust representation that easily captures the spatial-temporal evolutions of motions from skeleton sequences. Second, how to design D-CNNs capable of learning discriminative features from the new representation in a effective manner. To address these tasks, a skeleton-based representation, namely, SPMF (Skeleton Pose-Motion Feature) is proposed. The SPMFs are built from two of the most important properties of a human action: postures and their motions. Therefore, they are able to effectively represent complex actions. For learning and recognition tasks, we design and optimize new D-CNNs based on the idea of Inception Residual networks to predict actions from SPMFs. Our method is evaluated on two challenging datasets including MSR Action3D and NTU-RGB+D. Experimental results indicated that the proposed method surpasses state-of-the-art methods whilst requiring less computation.
PB IEEE
SN 978-1-4799-7061-2
YR 2018
FD 2018-09-06
LK https://hdl.handle.net/10016/28921
UL https://hdl.handle.net/10016/28921
LA eng
NO This paper has been presented at : 25th IEEE International Conference on Image Processing (ICIP)
DS e-Archivo
RD 18 jul. 2024