Anomaly detection in surveillance videos using deep learning
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Date
2020-06
Authors
Lu, Yiwei
Journal Title
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Abstract
We address the problem of anomaly detection in videos. The goal is to identify
unusual behaviors 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 thesis, 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.
Description
Keywords
Anomaly detection, Surveillance, Deep learning
Citation
Lu, Yiwei, et al. "Future Frame Prediction Using Convolutional VRNN for Anomaly Detection." 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2019.