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- Publisher Website: 10.1109/ICME.2017.8019325
- Scopus: eid_2-s2.0-85030226661
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Conference Paper: Remembering history with convolutional LSTM for anomaly detection
Title | Remembering history with convolutional LSTM for anomaly detection |
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Authors | |
Keywords | Anomaly detection Convolutional Neural Networks Long Short Term Memory |
Issue Date | 2017 |
Citation | Proceedings - IEEE International Conference on Multimedia and Expo, 2017, p. 439-444 How to Cite? |
Abstract | This paper tackles anomaly detection in videos, which is an extremely challenging task because anomaly is unbounded. We approach this task by leveraging a Convolutional Neural Network (CNN or ConvNet) for appearance encoding for each frame, and leveraging a Convolutional Long Short Term Memory (ConvLSTM) for memorizing all past frames which corresponds to the motion information. Then we integrate ConvNet and ConvLSTM with Auto-Encoder, which is referred to as ConvLSTM-AE, to learn the regularity of appearance and motion for the ordinary moments. Compared with 3D Convolutional Auto-Encoder based anomaly detection, our main contribution lies in that we propose a ConvLSTM-AE framework which better encodes the change of appearance and motion for normal events, respectively. To evaluate our method, we first conduct experiments on a synthesized Moving-MNIST dataset under controlled settings, and results show that our method can easily identify the change of appearance and motion. Extensive experiments on real anomaly datasets further validate the effectiveness of our method for anomaly detection. |
Persistent Identifier | http://hdl.handle.net/10722/345091 |
ISSN | 2020 SCImago Journal Rankings: 0.368 |
DC Field | Value | Language |
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dc.contributor.author | Luo, Weixin | - |
dc.contributor.author | Liu, Wen | - |
dc.contributor.author | Gao, Shenghua | - |
dc.date.accessioned | 2024-08-15T09:25:10Z | - |
dc.date.available | 2024-08-15T09:25:10Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Proceedings - IEEE International Conference on Multimedia and Expo, 2017, p. 439-444 | - |
dc.identifier.issn | 1945-7871 | - |
dc.identifier.uri | http://hdl.handle.net/10722/345091 | - |
dc.description.abstract | This paper tackles anomaly detection in videos, which is an extremely challenging task because anomaly is unbounded. We approach this task by leveraging a Convolutional Neural Network (CNN or ConvNet) for appearance encoding for each frame, and leveraging a Convolutional Long Short Term Memory (ConvLSTM) for memorizing all past frames which corresponds to the motion information. Then we integrate ConvNet and ConvLSTM with Auto-Encoder, which is referred to as ConvLSTM-AE, to learn the regularity of appearance and motion for the ordinary moments. Compared with 3D Convolutional Auto-Encoder based anomaly detection, our main contribution lies in that we propose a ConvLSTM-AE framework which better encodes the change of appearance and motion for normal events, respectively. To evaluate our method, we first conduct experiments on a synthesized Moving-MNIST dataset under controlled settings, and results show that our method can easily identify the change of appearance and motion. Extensive experiments on real anomaly datasets further validate the effectiveness of our method for anomaly detection. | - |
dc.language | eng | - |
dc.relation.ispartof | Proceedings - IEEE International Conference on Multimedia and Expo | - |
dc.subject | Anomaly detection | - |
dc.subject | Convolutional Neural Networks | - |
dc.subject | Long Short Term Memory | - |
dc.title | Remembering history with convolutional LSTM for anomaly detection | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/ICME.2017.8019325 | - |
dc.identifier.scopus | eid_2-s2.0-85030226661 | - |
dc.identifier.spage | 439 | - |
dc.identifier.epage | 444 | - |
dc.identifier.eissn | 1945-788X | - |