This project hosts the code for implementing the SiamCA algorithm for visual tracking, as presented in our paper:
The code based on the [PySOT](https://github.com/STVIR/pysot).
## Installation
Please find installation instructions in [`INSTALL.md`](INSTALL.md).
## Quick Start: Using siamca
### Add siamca to your PYTHONPATH
```bash
export PYTHONPATH=/path/to/siamca:$PYTHONPATH
See TRAIN.md for detailed instruction.
python tools/demo.py \
--config experiments/siamca_r50_l234/config.yaml \
--snapshot experiments/siamca_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/siamca_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/)
assume still in experiments/siamca_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
This project is released under the Apache 2.0 license.