Summary of Highly Efficient and Unsupervised Framework For Moving Object Detection in Satellite Videos, by C. Xiao et al.
Highly Efficient and Unsupervised Framework for Moving Object Detection in Satellite Videos
by C. Xiao, W. An, Y. Zhang, Z. Su, M. Li, W. Sheng, M. Pietikäinen, L. Liu
First submitted to arxiv on: 24 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an unsupervised framework for moving object detection in satellite videos (SVMOD), a challenging task due to the extremely dim and small target characteristics. The current learning-based methods require labor-intensive manual labels, high annotation costs, and contain tremendous computational redundancy. The proposed generic unsupervised framework uses pseudo labels generated by traditional methods that evolve during training to promote detection performance. Additionally, a sparse convolutional anchor-free detection network is designed to efficiently process dense multi-frame images by skipping redundant computation on background regions. This approach achieves both high efficiency (label and computation) and effectiveness, processing 98.8 frames per second on 1024×1024 images while achieving state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us detect objects moving in satellite videos without using lots of labeled data or doing a lot of extra calculations. Right now, we need to spend a lot of time and money labeling the pictures and then use powerful computers to find the objects. This new way of doing things is faster and better at finding objects than what’s currently available. It can even process really big images quickly! |
Keywords
» Artificial intelligence » Object detection » Unsupervised
