Recurrent Filter Learning for Visual Tracking

This is the implementation of our RFL tracker published in ICCV2017 workshop on VOT. Our code is written in python3(3.5) using Tensorflow(>=1.2) toolbox

For easy comparison, we upload our OTB100 results files to the main directory ./otb100_results.zip

Tracking

You use our pretrained model to test our tracker first.

1. Download the model from the link: model
2. Put the model into directory ./output/models
3. Run python3 tracking_demo.py in directory ./tracking

Training

1. Download the ILSRVC data from the official website and set proper paths for ISLVRC and their tfrecords in config.py
2. Then run the process_data.sh in ./data_preprocssing directory to convert ILSVRC data to tfrecords.
3. Run python3 train.py to train the model.

If you find the code is helpful, please cite

@inproceedings{Yang2017,
    author = {Yang, Tianyu and Chan, Antoni B.},
    booktitle = {ICCV Workshop on VOT},
    title = {Recurrent Filter Learning for Visual Tracking},
    url = {http://arxiv.org/abs/1708.03874},
    year = {2017}
}