robustOT

We build a benchmark to involve existing adversarial tracking attacks and defense methods and evaluate their performance, which could trick a series of novel works and push the progress to build a robust tracking system. This framework involves our recent tracking defense work published in ICLR 2024.

The detailed framework could be found in the following:

Installation

robustot incorporates the modern Python typing system for static type checking, making it easier to work with our tracking interfaces. Moreover, it utilizes the latest stable version of PyTorch for powering its tracking platform.

Requirements

• Python version ≥ 3.10
• Pytorch version ≥ 2.0.1

We recommend using conda to install the latest PyTorch:

conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia

Then, use pip to install the required packages:

pip install -r requirements/msot.txt

# dev only
pip install -r requirements/dev.txt

Setup ENV

Set up environment variables and make sure robustot and its dependencies are accessible to Python.

source env.sh

Gettings started

Quick start

• Testing

  We offer a unified interface for immediate testing on your configuration with any chosen tracker and processors. All available trackers are located in $MSOT_ROOT/configs/tests, and all processors are in $ROBOT_ROOT/configs.

  For instance, to start a test without any processors, utilize the following command:

  python $ROBOT_ROOT/scripts/test.py \
      -c $MSOT_ROOT/configs/tests/test_siamrpnpp_r50_otb.py \
      -d OTB100 \
      -o results
  

  Combining with arbitrary processors can be seamlessly achieved by passing the processor's config file at the test entry point. For example, to initiate a test with the CSA attack, use the command below:

  python $ROBOT_ROOT/scripts/test.py \
      -c $MSOT_ROOT/configs/tests/test_siamrpnpp_r50_otb.py \
      -d OTB100 \
      -p $ROBOT_ROOT/configs/builtin/crop.py \
      -p $ROBOT_ROOT/configs/atk/csa.py \
      -o results_csa

  For additional details, execute the following for a help message:

  python $ROBOT_ROOT/scripts/test.py --help

• Evaluation

  Evaluation can be performed using the evaluation entry point:

  python $ROBOT_ROOT/scripts/eval.py -d OTB100 -p results
  
  # To display the help message
  python $ROBOT_ROOT/scripts/eval.py --help

Processors

Bibtex

@article{chen2024lrr,
  title={LRR: Language-Driven Resamplable Continuous Representation against Adversarial Tracking Attacks},
  author={Chen, Jianlang and Ren, Xuhong and Guo, Qing and Juefei-Xu, Felix and Lin, Di and Feng, Wei and Ma, Lei and Zhao, Jianjun},
  journal={ICLR},
  year={2024}
}