Handling Occlusion in UAV Visual Tracking with Query-Guided Re-Detection

This is an official pytorch implementation of the 2024 IEEE Transactions on Instrumentation and Measurement paper:

Handling Occlusion in UAV Visual Tracking with Query-Guided Re-Detection
(accepted by IEEE Transactions on Instrumentation and Measurement; DOI: 10.1109/TIM.2024.3440378)

The paper can be downloaded from IEEE Xplore

The models and raw results can be downloaded from BaiduYun.

The tracking demos are displayed on the Bilibili or GitHub

The real-world tests are displayed on the Bilibili or GitHub

Proposed modules

• query update (QU) in Tracker

• Cross Fusion Layer (CFL) in model

• Trajectory prediction (TP) in Tracker

UAV Tracking

Datasets	qrdt_r50_l234
UAV20L - Full Occlusion (Suc./Pre.)	0.396/0.613
UAVDT - Large Occlusion (Suc./Pre.)	0.474/0.604
DTB70 - Occlusion (Suc./Pre.)	0.553/0.768
VisDrone2019-SOT-test-dev - Full Occlusion (Suc./Pre.)	0.591/0.812
HOB (Suc./Pre.)	0.363/0.244

Note:

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

Installation

Please find installation instructions in INSTALL.md.

Quick Start: Using QRDT

Add SmallTrack to your PYTHONPATH

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

demo

python tools/demo.py \
    --config experiments/siamban_r50_l234/config.yaml \
    --snapshot experiments/siamban_r50_l234/QRDT.pth
    --video demo/bag.avi

Download testing datasets

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.

Test tracker

cd experiments/siamban_r50_l234
python -u ../../tools/test.py 	\
	--snapshot QRDT.pth 	\ # model path
	--dataset UAV20L 	\ # 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/ssiamban_r50_l234

python ../../tools/eval.py 	 \
	--tracker_path ./results \ # result path
	--dataset UAV20L        \ # dataset name
	--num 1 		 \ # number thread to eval
	--tracker_prefix 'ch*'   # tracker_name

Training 🔧

See TRAIN.md for detailed instruction.

Acknowledgement

The code based on the PySOT , SiamBAN , CAM , CFME and DROL

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