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Abnormal event detection and localization in crowded scenes based on PCANet

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Abstract

Detecting and localizing abnormal events in crowded scenes still remains a challenging task among computer vision community. An unsupervised framework is proposed in this paper to address the problem. Low-level features and optical flows (OF) of video sequences are extracted to represent motion information in the temporal domain. Moreover, abnormal events usually occur in local regions and are closely linked to their surrounding areas in the spatial domain. To extract high-level information from local regions and model the relationship in spatial domain, the first step is to calculate optical flow maps and divide them into a set of non-overlapping sub-maps. Next, corresponding PCANet models are trained using the sub-maps at same spatial location in the optical flow maps. Based on the block-wise histograms extracted by PCANet models, a set of one-class classifiers are trained to predict the anomaly scores of test frames. The framework is completely unsupervised because it utilizes only normal videos. Experiments were carried out on UCSD Ped2 and UMN datasets, and the results show competitive performance of this framework when compared with other state-of-the-art methods.

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Acknowledgments

This work was supported by special funder from Chinese Academy of Sciences, with grant number XDA060112030.

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Authors and Affiliations

  1. School of Information Science and Technology, University of Science and Technology of China, Hefei, 230026, China

    Tianlong Bao, Saleem Karmoshi, Chunhui Ding & Ming Zhu

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  1. Tianlong Bao
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  2. Saleem Karmoshi
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  3. Chunhui Ding
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  4. Ming Zhu
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Correspondence to Tianlong Bao.

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Bao, T., Karmoshi, S., Ding, C. et al. Abnormal event detection and localization in crowded scenes based on PCANet. Multimed Tools Appl 76, 23213–23224 (2017). https://doi.org/10.1007/s11042-016-4100-0

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