Anomaly detection in surveillance videos using deep learning
| dc.contributor.author | Lu, Yiwei | |
| dc.contributor.examiningcommittee | Livi, Lorenzo (Computer Science and Mathematics) | en_US |
| dc.contributor.examiningcommittee | Ashraf, Ahmed (Electrical & Computer Engineering) | en_US |
| dc.contributor.supervisor | Wang, Yang (Computer Science) | en_US |
| dc.date.accessioned | 2020-07-20T20:28:34Z | |
| dc.date.available | 2020-07-20T20:28:34Z | |
| dc.date.copyright | 2020-07-20 | |
| dc.date.issued | 2020-06 | en_US |
| dc.date.submitted | 2020-06-04T17:40:19Z | en_US |
| dc.date.submitted | 2020-07-20T20:19:24Z | en_US |
| dc.degree.discipline | Computer Science | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.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. | en_US |
| dc.description.note | October 2020 | en_US |
| dc.identifier.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. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/34793 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Anomaly detection, Surveillance, Deep learning | en_US |
| dc.title | Anomaly detection in surveillance videos using deep learning | en_US |
| dc.type | master thesis | en_US |