Need help with DeepTracking?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

129 Stars 51 Forks 8 Commits 2 Opened issues


Source code of DeepTracking research project

Services available


Need anything else?

Contributors list

No Data

DeepTracking: Seeing Beyond Seeing Using Recurrent Neural Networks

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)
  • paper:
  • webpage:

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:
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.

Optional parameters


-gpu [id] use GPU id
-model [file] neural network model
-data [file] data for training
-iter [number] the number of training iterations
-N [number] the length of training sequences
-learningRate [number] learning rate
-initweights [file] initial weights
-grid[minX/maxX/minY/maxY/step] [number] 2D occupancy grid parameters
-sensor[start/step] 1D depth sensor parameters


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

Release Notes

Version 1.0

  • Original version from the academic paper.

Version 1.1

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

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.