This is an official implemention for โSiamCorners: Siamese Corner Networks for VisualTrackingโ. The code are available here now.
The overview of our SiamCorners architecture, which includes the Siamese feature extractor followed by the top-left corner and bottom-right corner
branches in parallel.
- Python 3.7
- PyTorch 1.0.0
- numpy
- CUDA 10
- skimage
- matplotlib
- GCC 4.9.2 or above
Prepare training dataset, detailed preparations are listed in training_dataset directory.
Compile the C++ implementation of the corner pooling layers. (GCC4.9.2 or above is required.)
cd <SiamCorners dir>/pysot/models/corners/py_utils/_cpools
python setup.py install --user
Compile the NMS code which are originally from Faster R-CNN and Soft-NMS.
cd <SiamCorners dir>/pysot/tracker/external
make
CUDA_VISIBLE_DEVICES=0,1
python -m torch.distributed.launch \
--nproc_per_node=2 \
--master_port=2333 \
../../tools/train.py --cfg config.yaml
python ../tools/test.py
If you're using this code in a publication, please cite our paper.
@InProceedings{SiamCorners,
author = {Kai Yang, Zhenyu He, Wenjie Pei, Zikun Zhou, Xin Li, Di Yuan and Haijun Zhang},
title = {SiamCorners: Siamese Corner Networks for Visual Tracking},
booktitle = {IEEE Transactions on Multimedia},
month = {April},
year = {2021}
}
Our anchor-free tracker is based on PySot and CornerNet. We sincerely thank the authors Bo Li and Hei Law for providing these great works.
If you have any questions, please feel free to contact [email protected]