تقرير
Few-shot Scene-adaptive Anomaly Detection
| العنوان: | Few-shot Scene-adaptive Anomaly Detection |
|---|---|
| المؤلفون: | Lu, Yiwei, Yu, Frank, Reddy, Mahesh Kumar Krishna, Wang, Yang |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning |
| الوصف: | We address the problem of anomaly detection in videos. The goal is to identify unusual behaviours automatically by learning exclusively from normal videos. Most existing approaches are usually data-hungry and have limited generalization abilities. They usually need to be trained on a large number of videos from a target scene to achieve good results in that scene. In this paper, we propose a novel few-shot scene-adaptive anomaly detection problem to address the limitations of previous approaches. Our goal is to learn to detect anomalies in a previously unseen scene with only a few frames. A reliable solution for this new problem will have huge potential in real-world applications since it is expensive to collect a massive amount of data for each target scene. We propose a meta-learning based approach for solving this new problem; extensive experimental results demonstrate the effectiveness of our proposed method. Comment: Accepted to ECCV 2020 as a spotlight paper |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2007.07843 |
| رقم الأكسشن: | edsarx.2007.07843 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |