[TITS2025] Target-Distractor Aware UAV Tracking via Global Agent

This is an official pytorch implementation of the 2025 IEEE Transactions on Intelligent Transportation Systems paper:

Global Agent-based Target-Distractor Aware for UAV Object Tracking 
(DOI: https://doi.org/10.1109/TITS.2025.3581391)

Tracker

TDAT

Object tracking is a basic task of the unmanned aerial vehicle (UAV)-based intelligent visual perception system. The presence of similar targets and complex backgrounds in the airborne perspective poses significant challenges to aerial trackers. However, existing target-aware or distractor-aware trackers fail to capture discriminative cues from both target and background information in a balanced manner, resulting in limited improvement. To address these issues, this paper proposes a global agent-based Target-Distractor Aware Tracker (TDAT) to enhance the discrimination of the target. TDAT comprises two effective modules: a global agent generator and an interactor. First, the generator aggregates the target and background regions into representative agents and then performs self-attention on these agents to explicitly model the global relationships between the target and backgrounds. Next, the interactor realizes the bidirectional information interaction between global agents and local regions via self-attention. Based on the global dependencies encoded in global agents, the interactor extracts target-oriented features and enhances the understanding of the target. TDAT embedded with target-distractor awareness effectively widens the gap between target and background distractors. Experimental results on multiple UAV benchmarks show that TDAT achieves outstanding performance with a speed of 34.5 frames/s. The code is available at https://github.com/xyl-507/TDAT.

The paper can be downloaded from IEEE Xplore

Models and Raw Results can be downloaded from GitHub

Models and Raw Results can be downloaded from Baidu

UAV Tracking

UAV Datasets (Suc./Pre.)	Super-DiMP (baseline)	TDAT (ours)
UAVDT	0.610 / 0.845	0.655 / 0.885
UAVTrack112	0.715 / 0.901	0.723 / 0.910
VisDrone2019-SOT-test-dev	0.629 / 0.825	0.651 / 0.865
UAV20L	0.626 / 0.818	0.674 / 0.879

Installation

This document contains detailed instructions for installing the necessary dependencied for **TDAT **. The instructions have been tested on Ubuntu 18.04 system.

Install dependencies

• Create and activate a conda environment

  conda create -n TDAT  python=3.7
  conda activate TDAT 

• Install PyTorch

  conda install -c pytorch pytorch=1.8.0 torchvision=0.9.0 cudatoolkit=10.2

• Install other packages

  conda install matplotlib pandas tqdm
  pip install opencv-python tb-nightly visdom scikit-image tikzplotlib gdown
  conda install cython scipy
  sudo apt-get install libturbojpeg
  pip install pycocotools jpeg4py
  pip install wget yacs
  pip install shapely==1.6.4.post2

• Setup the environment
  Create the default environment setting files.

  # Change directory to <PATH_of_TDAT>
  cd TDAT
  
  # Environment settings for pytracking. Saved at pytracking/evaluation/local.py
  python -c "from pytracking.evaluation.environment import create_default_local_file; create_default_local_file()"
  
  # Environment settings for ltr. Saved at ltr/admin/local.py
  python -c "from ltr.admin.environment import create_default_local_file; create_default_local_file()"

You can modify these files to set the paths to datasets, results paths etc.

• Add the project path to environment variables
  Open ~/.bashrc, and add the following line to the end. Note to change <path_of_TDAT> to your real path.

  export PYTHONPATH=<path_of_TDAT>:$PYTHONPATH
  

• Download the pre-trained networks
  Download the network for TDAT and put it in the directory set by "network_path" in "pytracking/evaluation/local.py". By default, it is set to pytracking/networks.

Quick Start

Traning

• Modify local.py to set the paths to datasets, results paths etc.
• Runing the following commands to train the TDAT. You can customize some parameters by modifying super_dimp.py

  conda activate TDAT
  cd TDAT/ltr
  python run_training.py dimp super_dimp

Test

• CUDA_VISIBLE_DEVICES=1

  python pytracking/run_experiment.py myexperiments uav_test --debug 0 --threads 0
  python pytracking/run_tracker.py dimp super_dimp --dataset_name uav --sequence bike1 --debug 0 --threads 0

Evaluation

• You can use pytracking to test and evaluate tracker. The results might be slightly different with PySOT due to the slight difference in implementation (pytracking saves results as integers, pysot toolkit saves the results as decimals).

Acknowledgement

The code based on the PyTracking , FasterViT. We would like to express our sincere thanks to the contributors.