Title :
Human Action recognition from depth videos using multi-projection based representation
Author :
Chien-Quang Le;Thanh Duc Ngo;Duy-Dinh Le;Shin´ichi Satoh;Duc Anh Duong
Author_Institution :
The Graduate University for Advanced Studies (SOKENDAI), Shonan Village, Hayama Kanagawa, 240-0193, Japan
Abstract :
In this paper, a novel method for human action recognition from depth videos is proposed. We project 3D data on to multiple 2D-planes from which dense trajectories features are extracted. In the training stage, for each projection, a classifier is trained using the training data. In the testing stage, for each test video, the multiple trained classifiers are applied and the predicted scores are combined for final decision. We propose a greedy-based method to select a subset of the trained classifiers for optimal combination. Experiments on the MSR Action 3D dataset show that the proposed method outperforms the baseline method that does not use multi-projection-based features.
Keywords :
"Indexes","Videos","Cameras"
Conference_Titel :
Multimedia Signal Processing (MMSP), 2015 IEEE 17th International Workshop on
DOI :
10.1109/MMSP.2015.7340879