Change Detection by Training a Triplet Netowk for Motion Feature Extraction

This repository provides demo code for:

Training a triplet model to learn motion feature.
Computing segmentation masks for any image sequence with an extracted MF-Net.

Repository Structure

The repository is structured as follows:

models contains the Triplet model to train our network
net contains our trained model
utils contain lua and CUDA code for our Change Detection algorithm. This needs to be compiled before execution.

Dependencies

This code requires:

torch7
opencv >= 2.4(for training)
png++ (for training)
cudnn
display package for torch7

Compute the Segmentation Mask

Download this repository:

$git clone https://github.com/TienPhuocNguyen/mf-net

First, we have to compile the shared libraries:

$cd mf-net/utils
$make

If errors occur, you should check the computing architecture of your GPU and modify the Makefile. Here, we use sm_61 for the Titan X Pascal. If success, the command will produce a file libcutils.so.

To display results on your screen, install the display package:

$luarocks install display

Launch the server:

$th -ldisplay.start 8000 0.0.0.0

Then open 0.0.0.0:8000 on your browser to open the remote desktop.

To execute the program, run the command:

$th run.lua

The file run.lua also provides some arguments to specify the directories of image sequence and trained model. For examples:

$th run.lua -n net/trained.t7 -s datasets/CDNet2014/dataset/dynamicBackground/fall/input