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, 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 plan to organize a challenge based on the recent VISO dataset, and focus on the specific challenges and research problems in moving object detection and tracking in satellite videos. We hope this challenge could inspire the community to explore the tough problems in satellite video analysis, and ultimately drive technological advancement in emerging applications.

Description of the Challenge

The 1st Challenge on Moving Object Detection and Tracking in Satellite Videos (SatVideoDT) at ICPR 2022 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 challenge is expected to include the following three competition tracks.

Datasets

This challenge is built upon our recently released 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 plane, car, ship and train, are manually labeled. An example of a labeled video is shown below:

Evaluation Metrics

Track 1: Moving object detection in satellite videos.

To evaluate the detection performance of the methods submitted to the challenge, 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: Single object tracking in satellite videos.

Following the standard evaluation protocol of the visual tracking OTB dataset, all trackers will be evaluated using two metrics: Distance Precision Rate (DPR) and Overlap Success Rate (OSR). Note that, the final results are ranked according to the AUC values of Distance Precision Rate (DPR) and Overlap Success Rate (OSR) calculated by the participants in the test dataset with a ratio of 50% and 50% respectively.

Track 3: Multiple-object tracking in satellite videos.

The metrics in generic Multiple-Object Tracking Challenge Benchmark will be used for quantitative evaluation. The final results of multi-objective tracking will be ranked according to the Multiple Object Tracking Accuracy (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 challenge, DSFNet is used as a detection baseline model and the submitted results should be at least on par with DSFNet. CFME is used as a single object tracking baseline model and the submitted results should be at least on par with CFME. 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. Track3.) to help participants to format their submissions. In the test phase, the final results and the source codes (top three participant teams) need to be submitted to email satvideodt@outlook.com. Please refer to our online website (Track1 Track2 Track3) for details of the submission rules.

Important Dates

Awards:

The organization committee of the ICPR2022 Conference will issue award certificates to the top three participant teams of each track. Teams with better grades will be invited to submit their co-written papers to the ICPR2022 Challenge 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 challenge or award.

SatVideoDT 2022 Terms and Conditions:

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

Issues and Questions:

For any question regarding this challenge, please send an email to yinqian18@nudt.edu.cn. You can also join our WeChat group by scanning the code below:

Citiation:

@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}
}

Organizers