期刊
PATTERN RECOGNITION LETTERS
卷 126, 期 -, 页码 132-138出版社
ELSEVIER
DOI: 10.1016/j.patrec.2018.05.004
关键词
Gait; Action
We face the problem of gait recognition by using a robust deep learning model based on graphs. The proposed graph based learning approach, named Time based Graph Long Short-Term Memory (TGLSTM) network, is able to dynamically learn graphs when they may change during time, like in gait and action recognition. Indeed, the TGLSTM model jointly exploits structured data and temporal information through a deep neural network model able to learn long short-term dependencies together with graph structure. The experiments were made on popular datasets for action and gait recognition, MSR Action 3D, CAD-60, CASIA Gait B, TUM Gait from Audio, Image and Depth (TUM-GAID) datasets, investigating the advantages of TGLSTM with respect to state-of-the-art methods . (C) 2018 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据