Name
Title | ||
|---|---|---|
Action classification in soccer videos with long short-term memory recurrent neural networks |
Abstract | ||
|---|---|---|
In this paper, we propose a novel approach for action classification in soccer videos using a recurrent neural network scheme. Thereby, we extract from each video action at each timestep a set of features which describe both the visual content (by the mean of a BoW approach) and the dominant motion (with a key point based approach). A Long Short-Term Memory-based Recurrent Neural Network is then trained to classify each video sequence considering the temporal evolution of the features for each timestep. Experimental results on the MICC-Soccer-Actions-4 database show that the proposed approach outperforms classification methods of related works (with a classification rate of 77%), and that the combination of the two features (BoW and dominant motion) leads to a classification rate of 92%. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15822-3_20 | ICANN (2) |
Keywords | Field | DocType |
novel approach,classification rate,bow approach,neural network,classification method,video action,long short-term memory recurrent,soccer video,video sequence,action classification,dominant motion,long short term memory,recurrent neural network | Pattern recognition,Convolutional neural network,Computer science,Long short term memory,Recurrent neural network,Artificial intelligence,Classification rate,Machine learning,Visual Word | Conference |
Volume | ISSN | ISBN |
6353 | 0302-9743 | 3-642-15821-8 |
Citations | PageRank | References |
18 | 1.28 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Moez Baccouche | 1 | 236 | 10.88 |
| Franck Mamalet | 2 | 302 | 16.35 |
| Christian Wolf | 3 | 1027 | 54.93 |
| Christophe Garcia | 4 | 562 | 49.05 |
| Atilla Baskurt | 5 | 654 | 43.97 |