  This thesis explore different approaches using Convolutional and Recurrent
Neural Networks to classify and temporally localize activities on videos,
furthermore an implementation to achieve it has been proposed. As the first
step, features have been extracted from video frames using an state of the art
3D Convolutional Neural Network. This features are fed in a recurrent neural
network that solves the activity classification and temporally location tasks
in a simple and flexible way. Different architectures and configurations have
been tested in order to achieve the best performance and learning of the video
dataset provided. In addition it has been studied different kind of post
processing over the trained network's output to achieve a better results on the
temporally localization of activities on the videos. The results provided by
the neural network developed in this thesis have been submitted to the
ActivityNet Challenge 2016 of the CVPR, achieving competitive results using a
simple and flexible architecture.
