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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
- Host: GitHub
- URL: https://github.com/akosiorek/hart
- Owner: akosiorek
- License: gpl-3.0
- Created: 2017-06-27T17:12:14.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-08-20T12:33:37.000Z (over 6 years ago)
- Last Synced: 2024-10-28T22:34:44.247Z (about 2 months ago)
- Topics: attention-mechanism, kitti-dataset, neural-nets, object-tracking, rnn, tensorflow
- Language: Python
- Homepage:
- Size: 23.8 MB
- Stars: 146
- Watchers: 18
- Forks: 49
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
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README
# 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. Runpython 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.