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.
TGLSTM: A time based graph deep learning approach to gait recognition
Alfredo Petrosino;Francesco Battistone
In corso di stampa
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
