Detecting spatiotemporal irregularities in videos via a 3D convolutional autoencoder
Spatiotemporal irregularities (i.e., the uncommon appearance and motion patterns) in videos are difficult to detect, as they are usually not well defined and appear rarely in videos. We tackle this problem by learning normal patterns from regular videos, while treating irregularities as deviations f...
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| Published in: | Journal of visual communication and image representation Vol. 67; p. 102747 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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01.02.2020
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| ISSN: | 1047-3203, 1095-9076 |
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| Abstract | Spatiotemporal irregularities (i.e., the uncommon appearance and motion patterns) in videos are difficult to detect, as they are usually not well defined and appear rarely in videos. We tackle this problem by learning normal patterns from regular videos, while treating irregularities as deviations from normal patterns. To this end, we introduce a 3D fully convolutional autoencoder (3D-FCAE) that is trainable in an end-to-end manner to detect both temporal and spatiotemporal irregularities in videos using limited training data. Subsequently, temporal irregularities can be detected as frames with high reconstruction errors, and irregular spatiotemporal patterns can be detected as blurry regions that are not well reconstructed. Our approach can accurately locate temporal and spatiotemporal irregularities thanks to the 3D fully convolutional autoencoder and the explored effective architecture. We evaluate the proposed autoencoder for detecting irregular patterns on benchmark video datasets with weak supervision. Comparisons with state-of-the-art approaches demonstrate the effectiveness of our approach. Moreover, the learned autoencoder shows good generalizability across multiple datasets. |
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| AbstractList | Spatiotemporal irregularities (i.e., the uncommon appearance and motion patterns) in videos are difficult to detect, as they are usually not well defined and appear rarely in videos. We tackle this problem by learning normal patterns from regular videos, while treating irregularities as deviations from normal patterns. To this end, we introduce a 3D fully convolutional autoencoder (3D-FCAE) that is trainable in an end-to-end manner to detect both temporal and spatiotemporal irregularities in videos using limited training data. Subsequently, temporal irregularities can be detected as frames with high reconstruction errors, and irregular spatiotemporal patterns can be detected as blurry regions that are not well reconstructed. Our approach can accurately locate temporal and spatiotemporal irregularities thanks to the 3D fully convolutional autoencoder and the explored effective architecture. We evaluate the proposed autoencoder for detecting irregular patterns on benchmark video datasets with weak supervision. Comparisons with state-of-the-art approaches demonstrate the effectiveness of our approach. Moreover, the learned autoencoder shows good generalizability across multiple datasets. |
| ArticleNumber | 102747 |
| Author | Meng, Jingjing Zhou, Chunluan Tan, Yap-Peng Tu, Zhigang Yan, Mengjia Yuan, Junsong |
| Author_xml | – sequence: 1 givenname: Mengjia orcidid: 0000-0002-6729-6518 surname: Yan fullname: Yan, Mengjia email: YANM0006@e.ntu.edu.sg organization: School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Ave., Singapore 639798, Singapore – sequence: 2 givenname: Jingjing surname: Meng fullname: Meng, Jingjing email: jmeng2@buffalo.edu organization: Computer Science and Engineering Department, University at Buffalo, the State University of New York, United States – sequence: 3 givenname: Chunluan surname: Zhou fullname: Zhou, Chunluan email: chunluan@buffalo.edu organization: Computer Science and Engineering Department, University at Buffalo, the State University of New York, United States – sequence: 4 givenname: Zhigang orcidid: 0000-0002-4395-6614 surname: Tu fullname: Tu, Zhigang email: tuzhigang@whu.edu.cn organization: State Key Lab of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Ruoyu Road 129, Wuhan, China – sequence: 5 givenname: Yap-Peng surname: Tan fullname: Tan, Yap-Peng email: eyptan@ntu.edu.sg organization: School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Ave., Singapore 639798, Singapore – sequence: 6 givenname: Junsong surname: Yuan fullname: Yuan, Junsong email: jsyuan@buffalo.edu organization: Computer Science and Engineering Department, University at Buffalo, the State University of New York, United States |
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| Keywords | Spatiotemporal irregularity detection 3D convolution Autoencoder Real-time Anomaly detection Unsupervised learning |
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| References_xml | – reference: W. Hong, Z. Wang, M. Yang, J. Yuan, Conditional generative adversarial network for structured domain adaptation, in: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2018. – reference: O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation, in: International Conference on Medical image computing and computer-assisted intervention, Springer, 2015, pp. 234–241. – reference: Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, T. Darrell, Caffe: Convolutional architecture for fast feature embedding, in: ACM MM, 2014, pp. 675–678. – reference: R. Mehran, A. Oyama, M. Shah, Abnormal crowd behavior detection using social force model, in: CVPR, 2009, pp. 935–942. – reference: A. Radford, L. Metz, S. 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| SubjectTerms | 3D convolution Anomaly detection Autoencoder Real-time Spatiotemporal irregularity detection Unsupervised learning |
| Title | Detecting spatiotemporal irregularities in videos via a 3D convolutional autoencoder |
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