Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark (2111.12960v1)

Abstract: Satellite video cameras can provide continuous observation for a large-scale area, which is important for many remote sensing applications. However, achieving moving object detection and tracking in satellite videos remains challenging due to the insufficient appearance information of objects and lack of high-quality datasets. In this paper, we first build a large-scale satellite video dataset with rich annotations for the task of moving object detection and tracking. This dataset is collected by the Jilin-1 satellite constellation and composed of 47 high-quality videos with 1,646,038 instances of interest for object detection and 3,711 trajectories for object tracking. We then introduce a motion modeling baseline to improve the detection rate and reduce false alarms based on accumulative multi-frame differencing and robust matrix completion. Finally, we establish the first public benchmark for moving object detection and tracking in satellite videos, and extensively evaluate the performance of several representative approaches on our dataset. Comprehensive experimental analyses and insightful conclusions are also provided. The dataset is available at https://github.com/QingyongHu/VISO.

• The paper introduces the VISO dataset with over 1.6M annotations for precise satellite video object tracking.
• It proposes a Motion Modeling Baseline that leverages multi-frame differencing and background modeling for enhanced precision and recall.
• The framework outperforms existing methods, offering significant advances for remote sensing and smart city applications.

An Examination of Techniques for Detecting and Tracking Small and Dense Moving Objects in Satellite Videos

The given paper presents a comprehensive paper on detecting and tracking moving objects in satellite videos, a topic of significant interest due to its applications in areas such as urban traffic management, ocean monitoring, and smart city initiatives. The authors introduce a novel large-scale satellite video dataset, referred to as VIdeo Satellite Objects (VISO), together with a benchmark for evaluating performance in moving object detection and tracking in the context of satellite data.

Research Contributions

A notable contribution of this work is the creation of the VISO dataset. This dataset comprises 47 high-resolution video sequences captured by the Jilin-1 satellite constellation and includes meticulous annotations of 1,646,038 object instances across various categories such as planes, cars, ships, and trains. Furthermore, the authors propose a Motion Modeling Baseline (MMB) that enhances detection performance by leveraging accumulative multi-frame differencing and background modeling through robust matrix completion. This methodology aims to tackle the typical obstacles faced in satellite video analysis, such as low resolution, insufficient appearance information, and complex, dynamic backgrounds.

Performance Evaluation

The implementation of the proposed framework was extensively evaluated against several representative methods. Key evaluation metrics include moving object detection performance, single-object tracking (SOT), and multi-object tracking (MOT) accuracy. The substantial dataset size and intricate object annotations make VISO a significant benchmark for satellite video-based tasks, setting it apart from previous datasets which have typically been smaller and lacked the detailed annotations necessary for robust model training and evaluation.

Results

Numerically, the proposed MMB demonstrated superior performance in terms of recall and precision compared to existing methods, as reflected in the comprehensive tables of results. Specifically, on individual video tests, the framework outperformed other methods, achieving the highest average precision and recall values. The framework's usage of both motion cues and temporal consistency effectively reduced false positives and improved the detection rate for tiny and slow-moving objects.

Implications and Future Directions

The insights drawn from this research have several implications. Practically, improved detection and tracking can advance applications in remote sensing, offering more reliable and detailed analyses for surveillance and environmental monitoring. Theoretically, the introduction of a benchmark suitable for challenging satellite videos can stimulate advancements in both detection algorithms and tracking methodologies, particularly under conditions of low-resolution input and densely populated scenes.

In considering future work, the paper highlights potential enhancements in motion trajectory modeling, domain adaptation to counter the difference in visual characteristics between aerial and ground-based imagery, and the utilization of deep learning architectures fine-tuned on large-scale satellite data. Furthermore, integrating techniques for super-resolution might offer avenues to mitigate low-resolution challenges.

In conclusion, the paper provides an important dataset and benchmark that are poised to drive research in an underexplored area of satellite video analysis. The proposed methodologies offer significant improvements in object detection and tracking, backed by solid empirical results. As satellite video usage continues to grow, such foundational work is essential in shaping future research trajectories and practical applications.