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https://github.com/akosiorek/hart

Hierarchical Attentive Recurrent Tracking
https://github.com/akosiorek/hart

attention-mechanism kitti-dataset neural-nets object-tracking rnn tensorflow

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Hierarchical Attentive Recurrent Tracking

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# Hierarchical Attentive Recurrent Tracking



This is an official Tensorflow implementation of single object tracking in videos by using hierarchical attentive recurrent neural networks, as presented in the following paper:



[A. R. Kosiorek](http://akosiorek.github.io), [A. Bewley](http://ori.ox.ac.uk/mrg_people/alex-bewley/), [I. Posner](http://ori.ox.ac.uk/mrg_people/ingmar-posner/), ["Hierarchical Attentive Recurrent Tracking", NIPS 2017](https://arxiv.org/abs/1706.09262).



* **Author**: Adam Kosiorek, Oxford Robotics Institue, University of Oxford

* **Email**: adamk(at)robots.ox.ac.uk

* **Paper**: https://arxiv.org/abs/1706.09262

* **Webpage**: http://ori.ox.ac.uk/



## Installation

Install [Tensorflow v1.1](https://www.tensorflow.org/versions/r1.1/install/) and the following dependencies

 (using `pip install -r requirements.txt` (preferred) or `pip install [package]`):

* matplotlib==1.5.3

* numpy==1.12.1

* pandas==0.18.1

* scipy==0.18.1



## Demo

The notebook `scripts/demo.ipynb` contains a demo, which shows how to evaluate tracker on an arbitrary image sequence. By default, it runs on images located in `imgs` folder and uses a pretrained model.

Before running the demo please download AlexNet weights first (described in the Training section).



## Data

    

1. Download KITTI dataset from [here](http://www.cvlibs.net/datasets/kitti/eval_tracking.php). We need [left color images](http://www.cvlibs.net/download.php?file=data_tracking_image_2.zip) and [tracking labels](http://www.cvlibs.net/download.php?file=data_tracking_label_2.zip).

2. Unpack data into a data folder; images should be in an image folder and labels should be in a label folder.

3. Resize all the images to `(heigh=187, width=621)` e.g. by using the `scripts/resize_imgs.sh` script.



## Training



1. Download the AlexNet weights:

    * Execute `scripts/download_alexnet.sh` or

    * Download the weights from [here](http://www.cs.toronto.edu/~guerzhoy/tf_alexnet/bvlc_alexnet.npy) and put the file in the `checkpoints` folder.

2. Run



        python scripts/train_hart_kitti.py --img_dir=path/to/image/folder --label_dir=/path/to/label/folder



The training script will save model checkpoints in the `checkpoints` folder and report train and test scores every couple of epochs. You can run tensorboard in the `checkpoints` folder to visualise training progress. Training should converge in about 400k iterations, which should take about 3 days. It might take a couple of hours between logging messages, so don't worry.



## Evaluation on KITTI dataset

The `scripts/eval_kitti.ipynb` notebook contains the code necessary to prepare (IoU, timesteps) curves for train and validation set of KITTI. Before running the evaluation:

* Download AlexNet weights (described in the Training section).

* Update image and label folder paths in the notebook.



## Citation



If you find this repo useful in your research, please consider citing:



    @inproceedings{Kosiorek2017hierarchical,

       title = {Hierarchical Attentive Recurrent Tracking},

       author = {Kosiorek, Adam R and Bewley, Alex and Posner, Ingmar},

       booktitle = {Neural Information Processing Systems},

       url = {http://www.robots.ox.ac.uk/~mobile/Papers/2017NIPS_AdamKosiorek.pdf},

       pdf = {http://www.robots.ox.ac.uk/~mobile/Papers/2017NIPS_AdamKosiorek.pdf},

       year = {2017},

       month = {December}

    }



## 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  .



## Release Notes

**Version 1.0**

* Original version from the paper. It contains the KITTI tracking experiment.