This is an official pytorch implementation of the 2023 Chinese Journal of Aeronautics paper:

Template-guided frequency attention and adaptive cross-entropy loss for UAV visual tracking
(accepted by Chinese Journal of Aeronautics, DOI: https://doi.org/10.1016/j.cja.2023.03.048)

The paper can be downloaded from Chinese Journal of Aeronautics

The models and raw results can be downloaded from BaiduYun.

Note:

• r50_lxyz denotes the outputs of stage x, y, and z in ResNet-50.

Please find installation instructions in INSTALL.md.

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

python tools/demo.py \
    --config experiments/siamban_mobilev2_l234/config.yaml \
    --snapshot experiments/siamban_mobilev2_l234/MobileTrack.pth
    --video demo/bag.avi

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

cd experiments/siamban_mobilev2_l234
python -u ../../tools/test.py 	\
	--snapshot TGFAT.pth 	\ # model path
	--dataset UAV123 	\ # dataset name
	--config config.yaml	  # config file

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

assume still in experiments/siamban_mobilev2_l234

python ../../tools/eval.py 	 \
	--tracker_path ./results \ # result path
	--dataset UAV123         \ # dataset name
	--num 1 		 \ # number thread to eval
	--tracker_prefix 'ch*'   # tracker_name

See TRAIN.md for detailed instruction.

The code based on the PySOT , SiamBAN , FcaNet and SiamCAN

We would like to express our sincere thanks to the contributors.