This project hosts the code for implementing the SiamBAN algorithm for visual tracking, as presented in our paper:
@inproceedings{siamban,
title={Siamese Box Adaptive Network for Visual Tracking},
author={Chen, Zedu and Zhong, Bineng and Li, Guorong and Zhang, Shengping and Ji, Rongrong},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={6668--6677},
year={2020}
}
The full paper is available here. The raw results are here or here, extraction code: um9k. The code based on the PySOT.
Examples of SiamBAN outputs. The green boxes are the ground-truth bounding boxes of VOT2018, the yellow boxes are results yielded by SiamBAN.
Please find installation instructions in INSTALL.md.
export PYTHONPATH=/path/to/siamban:$PYTHONPATHDownload models in Model Zoo and put the model.pth in the correct directory in experiments
python tools/demo.py \
--config experiments/siamban_r50_l234/config.yaml \
--snapshot experiments/siamban_r50_l234/model.pth
# --video demo/bag.avi # (in case you don't have webcam)Download datasets and put them into testing_dataset directory. Jsons of commonly used datasets can be downloaded from here or here, extraction code: 8fju. If you want to test tracker on new dataset, please refer to pysot-toolkit to setting testing_dataset.
cd experiments/siamban_r50_l234
python -u ../../tools/test.py \
--snapshot model.pth \ # model path
--dataset VOT2018 \ # dataset name
--config config.yaml # config fileThe testing results will in the current directory(results/dataset/model_name/)
assume still in experiments/siamban_r50_l234
python ../../tools/eval.py \
--tracker_path ./results \ # result path
--dataset VOT2018 \ # dataset name
--num 1 \ # number thread to eval
--tracker_prefix 'model' # tracker_nameSee TRAIN.md for detailed instruction.
This project is released under the Apache 2.0 license.