• The implementation for "Satellite Video Single Object Tracking: A Systematic Review and An Oriented Object Tracking Benchmark".
• ISPRS Journal of Photogrammetry and Remote Sensing, 2024.

🏃Keep updating🏃:

• Results of DF on SatSOT have been released.

• Results of DF on SV248S have been released.

• Results of DF on OOTB have been released.

• Results of RAMC on SatSOT have been released.

• Results of RAMC on SV248S have been released.

• Results of RAMC on OOTB have been released.

• Results of REPS on OOTB have been released.

• The LMOD: A Large-scale and Multiclass Moving Object Detection Dataset for Satellite Videos has been released.

• Download the OOTB dataset by Baidu Cloud Disk (code: OOTB) or Google Drive to your disk, the organized directory looks like:

  --OOTB/
  	|--car_1/
              |--img/
                  |--0001.jpg
                  |--...
                  |--0268.jpg
              |--groundtruth.txt
  	|...
  	|--train_10/
              |--img/
                  |--0001.jpg
                  |--...
                  |--0120.jpg
              |--groundtruth.txt
  	|--anno/
  	    	|--car_1.txt
  	    	|...
  	    	|--train_10.txt
    	|--OOTB.json
  

• Download the benchmark Toolkit.

• Download the vlfeat.

• Installation

  Environments

  Matlab 2018a
  

• Visual samples

  Train_1 Plane_21

Single object tracking (SOT) in satellite video (SV) enables the continuous acquisition of position and range information of an arbitrary object, showing promising value in remote sensing applications. However, existing trackers and datasets rarely focus on the SOT of oriented objects in SV. To bridge this gap, this article presents a comprehensive review of various tracking paradigms and frameworks covering both the general video and satellite video domains and subsequently proposes the oriented object tracking benchmark (OOTB) to advance the field of visual tracking. OOTB contains 29,890 frames from 110 video sequences, covering common satellite video object categories including car, ship, plane, and train. All frames are manually annotated with oriented bounding boxes, and each sequence is labeled with 12 fine-grained attributes. Additionally, a high-precision evaluation protocol is proposed for comprehensive and fair comparisons of trackers. To validate the existing trackers and explore frameworks suitable for SV tracking, we benchmark 33 state-of-the-art trackers totaling 58 models with different features, backbones, and tracker tags. Finally, extensive experiments and insightful thoughts are also provided to help understand their performance and offer baseline results for future research. The proposed OOTB will be available at https://github.com/YZCU/OOTB.

• Experimental results for 33 SOTAs with a total of 58 models.
• Overall results for the top 30 trackers on OOTB.
• The precision plot (column 1), normalized precision plot (column 2), and success plot (column 3) for top 30 trackers. Rows 1 to 5 show the results for car, ship, plane, and train, respectively.
• The success plot of each attribute for the top 30 trackers.
• Qualitative results for nine SOTAs. The current frame is shown in the upper left corner.

If you have any questions or suggestions, feel free to contact me.
Email: yuzeng_chen@whu.edu.cn, rs_devotee@163.com

❤️❤️❤️ We really appreciate excellent works, as follows:

• OTB
• TrackingNet
• VOT

If you find our work helpful in your research, kindly consider citing it. We appreciate your support！

@article{CHEN2024212,
title = {Satellite video single object tracking: A systematic review and an oriented object tracking benchmark},
author = {Yuzeng Chen and Yuqi Tang and Yi Xiao and Qiangqiang Yuan and Yuwei Zhang and Fengqing Liu and Jiang He and Liangpei Zhang},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {210},
pages = {212-240},
year = {2024},
issn = {0924-2716},
doi = {https://doi.org/10.1016/j.isprsjprs.2024.03.013},
url = {https://www.sciencedirect.com/science/article/pii/S0924271624000856}
}