تقرير
Anomaly Detection in Satellite Videos using Diffusion Models
العنوان: | Anomaly Detection in Satellite Videos using Diffusion Models |
---|---|
المؤلفون: | Awasthi, Akash, Ly, Son, Nizam, Jaer, Zare, Samira, Mehta, Videet, Ahmed, Safwan, Shah, Keshav, Nemani, Ramakrishna, Prasad, Saurabh, Van Nguyen, Hien |
سنة النشر: | 2023 |
المجموعة: | Computer Science |
مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning |
الوصف: | The definition of anomaly detection is the identification of an unexpected event. Real-time detection of extreme events such as wildfires, cyclones, or floods using satellite data has become crucial for disaster management. Although several earth-observing satellites provide information about disasters, satellites in the geostationary orbit provide data at intervals as frequent as every minute, effectively creating a video from space. There are many techniques that have been proposed to identify anomalies in surveillance videos; however, the available datasets do not have dynamic behavior, so we discuss an anomaly framework that can work on very high-frequency datasets to find very fast-moving anomalies. In this work, we present a diffusion model which does not need any motion component to capture the fast-moving anomalies and outperforms the other baseline methods. |
نوع الوثيقة: | Working Paper |
URL الوصول: | http://arxiv.org/abs/2306.05376 |
رقم الأكسشن: | edsarx.2306.05376 |
قاعدة البيانات: | arXiv |
كن أول من يترك تعليقا!