Attention-based Fusion Router for RGBT Tracking. arXiv
We use the LasHeR training set for training, GTOT, RGBT210, RGBT234, LasHeR testing set, VTUAVST for testing, and their project addresses are as follows:
Clone repo:
git clone https://github.com/Alexadlu/AFter.git
cd AFter
Our code is trained and tested with Python == 3.8, PyTorch == 1.8.1 and CUDA == 11.2 on NVIDIA GeForce RTX 4090, you may use a different version according to your GPU.
conda create -n after python=3.8.13
conda activate after
pip install -r requirements.txt
$PROJECT_ROOT$/ltr/admin/local.py
. $PROJECT_ROOT$/ltr/models/pretrained
.python ltr/run_training.py --train_module tomp --train_name tomp50_v1
$PROJECT_ROOT$/pytracking/evaluation/environment.py
$PROJECT_ROOT$/pytracking/networks
and select the checkpoint name in $PROJECT_ROOT$/pytracking/parameter/tomp/tomp50.py
. Or just modify the checkpoint path in $PROJECT_ROOT$/pytracking/parameter/tomp/tomp50.py
. python pytracking/run_tracker.py --tracker_name tomp --tracker_param tomp50 --runid 8600 --dataset_name lashertestingset
Model | GTOT(PR/SR) | RGBT210(PR/SR) | RGBT234(PR/SR) | LasHeR(PR/NPR/SR) | VTUAV(PR/SR) | Checkpoint | Raw Result |
---|---|---|---|---|---|---|---|
AFter | 91.6 / 78.5 | 87.6 / 63.5 | 90.1 / 66.7 | 70.3 / 65.8 / 55.1 | 84.9 / 72.5 | download | download |
Our project is based on the pytracking framework and ToMP. Thanks for their contributions which help us to quickly implement our ideas.