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ELGLT

Code and results for TPAMI paper: Effective Local and Global Search for Fast Long-term Tracking

framework

Our code has been tested on

  • RTX 2080Ti GPU
  • Intel i9-9900K CPU / 64 GB Memory
  • Ubuntu 18.04.2 LTS
  • Python3.6
  • PyTorch1.2
  • CUDA10.0 / cuDNN7.6

Installation

# create conda env
$ conda create --name <env_name> python=3.6
$ source activate <env_name>

# install requirements
$ conda install pytorch=1.2.0 cudatoolkit=10.0 cudnn torchvision -c pytorch
$ pip install opencv-python
$ pip install pyyaml yacs tqdm colorama matplotlib cython tensorboardX
$ pip install scikit-image

# install tensorflow and keras for reproducing the tracker '***+skim'
$ pip install tensorflow-gpu==1.14.0
$ pip install keras==2.2.5
$ conda install numpy=1.16.4 # solve "FutureWarning: Passing (type, 1) ... type is deprecated"

# install base tracker
$ cd ./modules/pysot/
$ python setup.py build_ext --inplace
$ cd ../../

# install roi_align
$ cd ./RoIAlign
$ python setup.py install
$ cd ../

# install pytorch_nms
$ cd ./pytorch_nms
$ python setup.py install
$ cd ../

Experiments

  • Unzip [model.zip] to ./<root_path>, [skim.zip] to ./modules/skim;

  • Modify project_path and dataset_path in config_path.py.

  • You can reproduce the results by running the corresponding script in ./run_<dataset> .

Reproduce the Experiment in Table10

To fully evaluate the proposed re-detection module, we integrate our re-detection module into several trackers or replace the original re-detection module of some long-term trackers with ours. To reproduce this experiment:

  • Please integrate our scripts into corresponding trackers. You can find these scripts in ./run_VOT18LT/Effectiveness_of_Re-detector/<tracker_name>.

  • Download trackers: [LCT], [SiamMask], [SPLT], [DaSiam_LT & SiamVGG]

All results shown in our paper can be found in [GoogleDrive]. If you want to re-train the models, please refer to the corresponding code in ./modules

Citation

If you feel our work is useful, please cite:

@article{Zhao_TPAMI_ELGLT,
    author = {Haojie Zhao and Bin Yan and Dong Wang and Xuesheng Qian and Xiaoyun Yang and Huchuan Lu},
    title = {Effective Local and Global Search for Fast Long-term Tracking},
    journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
    year = {2022}
}

If you have any questions, you can contact me by email.

elglt's People

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elglt's Issues

重检测模块

请问重建测模块的训练数据集是什么样的格式呀?重建测模块训练部分train_guided2.py没调通.我已经给了数据集路径.但是env = lmdb.open(self.data_path[name], readonly=True, lock=False, readahead=False, meminit=False) 这句代码报找不到我的数据集路径的错误. 拜托方便的时候回一下哦,万分感谢!

Problem in downloading the model.zip and skim.zip!!!

Hello! Thank you for sharing your project!
Because it is not convinient for me to download model.zip and skim.zip in google drive, can you share the models on baidu cloud?
Thanks again for your excellent work!

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