Few-shot Scene-adaptive Anomaly Detection
This addresses the high cost of data collection for anomaly detection in real-world applications by enabling adaptation to new scenes with minimal data.
The paper tackles the problem of anomaly detection in videos by proposing a few-shot scene-adaptive approach that learns to detect anomalies in unseen scenes with only a few frames, achieving effective results as demonstrated in experiments.
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.