lemonsakura/unsupervised-tube-extraction

Unsupervised Tube Extraction

MATLABMIT

Unsupervised Tube Extraction using Transductive Learning and Dense Trajectories

Introduction

Algorithm used for unsupervised action tube extraction from videos. The method is described here.

This repository contains a Matlab implementation of the code, and has been tested on Linux, using Matlab R2015a.

Citing

If you find this unsupervised tube extraction algorithm useful in your research, please consider citing:

@inproceedings{marian2015unsupervised,
   title={Unsupervised Tube Extraction Using Transductive Learning and Dense Trajectories},
   author={Marian Puscas, Mihai and Sangineto, Enver and Culibrk, Dubravko and Sebe, Nicu},
   booktitle={Proceedings of the IEEE International Conference on Computer Vision},
   pages={1653--1661},
   year={2015}
}

License

The algorithm is under the MIT License, details in LICENSE

Instructions

extract the absolute coordinates of the trajectories throughout the video using Improved Dense Trajectories with default parameters.
- to maintain a roughly constant number of trajectories in the last frames of the video, we have mirrored the last 3 frames.
for cnn feature extraction use the bvlc_reference_caffenet model - fc7 features

Requirements