SiamBAN

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 Conference on Computer Vision and Pattern Recognition},
  year={2020}
}

The full paper is available here. The raw results are here. The code based on the PySOT.

Example SiamBAN outputs.

Installation

Please find installation instructions in INSTALL.md.

Quick Start: Using SiamBAN

Add SiamBAN to your PYTHONPATH

export PYTHONPATH=/path/to/siamban:$PYTHONPATH

Download models

Download models in Model Zoo and put the model.pth in the correct directory in experiments

Webcam demo

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 testing datasets

Download datasets and put them into testing_dataset directory. Jsons of commonly used datasets can be downloaded from here. If you want to test tracker on new dataset, please refer to pysot-toolkit to setting testing_dataset.

Test tracker

cd experiments/siamban_r50_l234
python -u ../../tools/test.py 	\
	--snapshot model.pth 	\ # model path
	--dataset VOT2018 	\ # dataset name
	--config config.yaml	  # config file

The testing results will in the current directory(results/dataset/model_name/)

Eval tracker

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_name

Training 🔧

See TRAIN.md for detailed instruction.

License

This project is released under the Apache 2.0 license.