CNN-LSTM Architecture for Action Recognition in Videos
Action recognition in videos is currently a topic of interest in the area of computer vision, due to potential applications such as: multimedia indexing, surveillance in public spaces, among others. In this paper we propose a CNN{LSTM architecture. First, a pre-trained VGG16 convolutional neuronal n...
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| Autores principales: | , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2019
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/89144 |
| Aporte de: |
| Sumario: | Action recognition in videos is currently a topic of interest in the area of computer vision, due to potential applications such as: multimedia indexing, surveillance in public spaces, among others. In this paper we propose a CNN{LSTM architecture. First, a pre-trained VGG16 convolutional neuronal networks extracts the features of the input video. Then, a LSTM classi es the video in a particular class.
To carry out the training and the test, we used the UCF-11 dataset.
Evaluate the performance of our system using the evaluation metric in accuracy. We apply LOOCV with k = 25, we obtain ~ 98% and ~ 91% for training and test respectively. |
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