Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks

by   Alberto Montes, et al.
Universitat Politècnica de Catalunya
ETH Zurich

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.


Untrimmed Video Classification for Activity Detection: submission to ActivityNet Challenge

Current state-of-the-art human activity recognition is focused on the cl...

The Devil Is in the Details: An Efficient Convolutional Neural Network for Transport Mode Detection

Transport mode detection is a classification problem aiming to design an...

Convolutional Drift Networks for Video Classification

Analyzing spatio-temporal data like video is a challenging task that req...

Dynamic Action Recognition: A convolutional neural network model for temporally organized joint location data

Motivation: Recognizing human actions in a video is a challenging task w...

Temporal HeartNet: Towards Human-Level Automatic Analysis of Fetal Cardiac Screening Video

We present an automatic method to describe clinically useful information...

Activity Recognition From Newborn Resuscitation Videos

Objective: Birth asphyxia is one of the leading causes of neonatal death...

Temporally Distributed Networks for Fast Video Segmentation

We present TDNet, a temporally distributed network designed for fast and...

Please sign up or login with your details

Forgot password? Click here to reset