News and Updates

2024-04-30: Training and validation data has been released (google Drive, Baidu Drive (Extraction code: VISO)). Paticipants can use the released data to develop their algorithms.

2024-06-20: Test server online, final test data has been released.Baidu Drive (Extraction code: VISO).


Introduction

Satellite video cameras can provide continuous observation for a large-scale area, which is suitable for several downstream remote sensing applications including traffic management, ocean monitoring, and smart city. Recently, small moving objects detection and tracking in satellite videos have attracted increasing attention in both academia and industry. However, it remains challenging to achieve accurate and robust moving object detection and tracking in satellite videos, due to the lack of high-quality and well-annotated public datasets and comprehensive benchmarks for performance evaluation. To this end, we organize this competition based on the recent VISO (google Drive, Baidu Drive (Extraction code: VISO)) dataset, and focus on the specific competitions and research problems in moving object detection and tracking in satellite videos. We hope this competition could inspire the community to explore the tough problems in satellite video analysis, and ultimately drive technological advancement in emerging applications.


Description of the Competition

ICPR 2024 competition on Moving Object Detection and Tracking in Satellite Videos aims to facilitate the development of video object detection and tracking algorithms, and push forward research in the field of moving object detection and tracking from satellite videos. This competition is expected to include the following two competition tracks.

Track 1: Tiny moving object detection in satellite videos.

VISO (google Drive, Baidu Drive (Extraction code: VISO)) dataset with 95 satellite videos (with 28,500 frames) captured by Jilin-1 satellite platforms, the goal of this task is to achieve moving object detection across the whole video. The organizers will provide the training set (with 21,000 frames) and the validation set (with 3000 frames) with full bounding boxes annotations. The test set (with 4500 frames) will be also provided, but with satellite images only. The participants are expected to train their models on the training set and validate the performance on the validation set. Then, the finalized model is used to generate detection results on the test set. The final performance will be automatically evaluated by the organizers with a set of objective quantitative metrics. (see Evaluation Metrics, Track 1).

Track 2: Multiple-object tracking in satellite videos.

This task aims at locating multiple objects of interest, maintaining their identities, and yielding their individual trajectories across the whole video. For this task, 95 sequences (videos 1 to 95) with a total of 28,500 frames from the VISO (google Drive, Baidu Drive (Extraction code: VISO)) dataset will be provided. Specifically, videos 1 to 65 will be used as the training set and videos 66 to 75 will be used as the validation set. The bounding box annotations and the instance id of each object in each frame will be provided. The test set is composed of videos 76 to 95, and only the annotation of the first frame will be provided for initialization. The participants are expected to train their models on the training set and validate the performance on the validation set. Then, the finalized model is used to generate tracking results on the test set.


Datasets

This competition is built upon our recently released VISO (google Drive, Baidu Drive (Extraction code: VISO)) dataset, the first well-annotated large-scale satellite videos dataset for the task of moving object detection and tracking. The dataset is captured by the Jilin-1 satellite constellation at different positions of the satellite orbit. The recorded videos cover several square kilometers of areas in real scenes. Each image in the videos has a resolution of 12,000 × 5,000 and contains a great number of objects with different scales. Moreover, four common types of moving objects, including car and ship, are manually labeled. An example of a labeled video is shown below:


Evaluation Metrics

Track 1: Tiny moving object detection in satellite videos.

To evaluate the detection performance of the methods submitted to the competition, the commonly-used evaluation metrics (i.e., mAP) for object detection will be used. We report the average results over all the satellite videos in the evaluation dataset. Note that, the final results are ranked by mAP (IOU = 0.5) calculated in the test dataset.

Track 2: Multiple-object tracking in satellite videos.

The metrics in generic multiple-object tracking competition benchmark will be used for quantitative evaluation. The final results of multi-objective tracking will be ranked according to the MOTA and IDF1 values calculated by participants in the test data set with a comprehensive weighting of 50% and 50% respectively.


Baseline Model

Over the last few years, several milestone methods have been developed for satellite videos, including DSFNet and CFME. In this competition, DSFNet is used as a detection baseline model and the submitted results should be at least on par with DSFNet. In particular, we selected SORT as a multi object tracking baseline model. Note that, the inputs (i.e., detection results at each frame) to the baselines is used the detection results achieved by DSFNet method. The solutions with evaluation metrics values lower than these baselines will not be ranked in the leaderboard.


Submission

We use CodaLab for online submission in the development phase. Here, we provide an example (Track1, Track2) to help participants to format their submissions. In the test phase, the final results and the source codes (both training and test) need to be submitted to email satvideodt2024@outlook.com. Please refer to our online website (Track1, Track2) for details of the submission rules.


Important Dates


Release of part of training and validation data; Apr 30, 2024
Registration deadline; Jun 15, 2024
Final test data release, testing server online; Jun 20, 2024
Test result submission deadline; Jul 10, 2024 (23:59 Pacific time)
Fact sheet / code / model submission deadline; Jul 10, 2024 (23:59 Pacific time)
Test preliminary score release to the participants; Jul 30, 2024
Report submission deadline (optional); Aug 10, 2024


Awards:

The organization committee of the ICPR2024 conference will issue award certificates to the top two participant teams of each track. Teams with better grades will be invited to submit their co-written papers to the ICPR2024 competition for peer review. If the paper is to be accepted and published, the participating team must specify the solution and ensure the repeatability of the competition results. Co-written paper is optional and does not affect the competitor's participation in the competition or award.


Citation:

@article{yin2021detecting,
title={Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark},
author={Yin, Qian and Hu, Qingyong and Liu, Hao and Zhang, Feng and Wang, Yingqian and Lin, Zaiping and An, Wei and Guo, Yulan},
journal={IEEE Transactions on Geoscience and Remote Sensing},
year={2021},
publisher={IEEE}
}


SatVideoDT 2024 Terms and Conditions:

Each group cannot have more than six group members (i.e., 1 to 5 group members is OK), and each paricipant can only join one group. Each group can only submit one algorithm for final ranking.


Issues and Questions:

For any question regarding this competition, please send an email to satvideodt2024@outlook.com. You can also join our WeChat group by scanning the code below:


Organizers

Yulan Guo
National University of Defense Technology
Qian Yin
National University of Defense Technology
Qingyong Hu
University of Oxford
Feng Zhang
National University of Defense Technology


Ye Zhang
Sun Yat-Sen University
Huaiyu Chen
National University of Defense Technology
Yuting Xie
National University of Defense Technology
Hanyun Wang
Information Engineering University