paper

Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks

arXiv:1608.08128

Abstract

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.

Best Poster Award at the 1st NIPS Workshop on Large Scale Computer Vision Systems (Barcelona, December 2016). Source code available at https://imatge-upc.github.io/activitynet-2016-cvprw/

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