The code for paper “Local Point Matching For Collaborative Image Registration and UAV Tracking”
Comparison of tracking pipeline. Current prevailing methods usually can’t
connect register, fusion, or track. Our framework can integrate these tasks into a whole.
The overview of our framework. It contains the DMR module, the LMIF
module, the KTT module, and the OAC module.
Notice that I created two separate virtual environments corresponding to the fusion model and the tracking model.
They each correspond to a requirements.txt in subfolder.
You can use it by:
pip install -r requirements.txt
For the 1st Anti-UAV datasets you can find in the official repository for Anti-UAV
Runs according to the framework in four modules:
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We should examine the trajectories of the two modalities separately. Configure the environment to download the offical pretrained reference model from
,and we can use it by run:
python tracking/demo.py -
The affine transformation matrix is then calculated by run:
python tracking/affine matric.py
and we can get aff_VIS images in ./Myoutputs/
-
Before fusing，we need to layer the infrared and registered visible images by "fusing/matlab_code_for_creating_base_and_detail_layers/main.m"
-
and then we can fuse by run
python fusing/test_image.pyFinally, we can get the fused Anti-UAV dataset in "fusing/outputs"
- In order to use Keypoint Trajectory Tracking module, we can run by this:
python tracking/demo1.py
- Eventually, we can get the prediction target box by run:
python tracking/creat_bbox.py
To evaluate the anti-UAV problem, we can run:
python tracking/Metric.py
Our approach outperforms existing SOTA trackers on the Anti-UAV dataset, showing substantial improvements in tracking performance in diverse challenging scenarios.
More details on the results of the experiment can be found here