Source code of DeepTracking research project
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README.md

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.

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

Installation

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

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

Data

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.

Training

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

Flag Description
-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

License

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/.

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.