Name
Title | ||
|---|---|---|
Activity recognition with echo state networks using 3D body joints and objects category. |
Abstract | ||
|---|---|---|
In this paper we present our experiments with an echo state network (ESN) for the task of classifying high-level human activities from video data. ESNs are recurrent neural networks which are biologically plausible, fast to train and they perform well in processing arbitrary sequential data. We focus on the integration of body motion with the information on objects manipulated during the activity, in order to overcome the visual ambiguities introduced by the processing of articulated body motion. We investigate the outputs learned and the accuracy of classification obtained with ESNs by using a challenging dataset of long high-level activities. We finally report the results achieved on this dataset. |
Year | Venue | Field |
|---|---|---|
2016 | ESANN | Sequential data,Activity recognition,Pattern recognition,Computer science,Recurrent neural network,Echo state network,Artificial intelligence,Reservoir computing,Machine learning,Body joints |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luiza Mici | 1 | 5 | 2.16 |
| Xavier Hinaut | 2 | 36 | 7.93 |
| Stefan Wermter | 3 | 1100 | 151.62 |