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Abnormal event detection for video surveillance using deep one-class learning

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Abstract

Abnormal event detection and localization is a challenging research problem in intelligent video surveillance. It is designed to automatically identify abnormal events from monitoring videos. The main difficulty of this task lies in that there is only one class called “normal event” in training video sequences. In recent years, many advanced algorithms have been proposed on the basis of hand-crafted features. Only a few algorithms are based on high-level features, but almost all these methods use two-stage learning. In this paper, we propose a novel end-to-end model which integrates the one-class Support Vector Machine (SVM) into Convolutional Neural Network (CNN), named Deep One-Class (DOC) model. Specifically, the robust loss function derived from the one-class SVM is proposed to optimize the parameters of this model. Compared with the hierarchical models, our model not only simplifies the complexity of the process, but also obtains the global optimal solution of the whole process. In the experiments, we validate our DOC model with a publicly available dataset and compare it with some state-of-art methods. The comparison results demonstrate that our model has great performance and it is effective for abnormal events detection from surveillance videos.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (grants No. 61672133 and No. 61632007), and the Fundamental Research Funds for the Central Universities (grants No. ZYGX2015J058 and No. ZYGX2014Z007).

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Authors and Affiliations

  1. Center for Future Media, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Jiayu Sun, Jie Shao & Chengkun He

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  1. Jiayu Sun
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  2. Jie Shao
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  3. Chengkun He
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Correspondence to Jie Shao.

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Sun, J., Shao, J. & He, C. Abnormal event detection for video surveillance using deep one-class learning. Multimed Tools Appl 78, 3633–3647 (2019). https://doi.org/10.1007/s11042-017-5244-2

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  • DOI: https://doi.org/10.1007/s11042-017-5244-2

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