This is an official Torch 7 implementation of the method for the end-to-end object tracking from occluded sensor measurements using neural network presented in the academic paper:

P. Ondruska and I. Posner, "Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks", in The Thirtieth AAAI Conference on Artificial Intelligence (AAAI), Phoenix, Arizona USA, 2016.

• author: Peter Ondruska, Mobile Robotics Group, University of Oxford
• email: ondruska(at)robots.ox.ac.uk
• paper: http://www.robots.ox.ac.uk/~mobile/Papers/2016AAAI_ondruska.pdf
• webpage: http://mrg.robots.ox.ac.uk/

For any questions about the code or the method please contact the author.

Install Torch 7 and the following dependencies (using luarocks install [package]):

• nngraph
• image
• cunn (optional for training on a GPU)

Download and unzip the training data for the simulated moving balls scenario:

http://mrg.robots.ox.ac.uk:8080/MRGData/deeptracking/DeepTracking_1_1.t7.zip

This is a native Torch 7 file format.

To train the model run:

th train.lua

Training of the neural network using provided data takes about 12 hours on Nvidia Titan X. Every 1000 iterations the training error is logged to log_model.txt, network weights are saved to weights_model and the visualisation of its performance is stored to video_model.

This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.

• Original version from the academic paper.

• Native decoding of raw 1D depth data into 2D input.
• Larger NN network.