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Abnormal event detection and localization using level set based on hybrid features

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Abstract

In this paper, a new method using level set is proposed for abnormal event detection and localization in video surveillance. From an input video, five image descriptors, namely the color moments, the edge histogram descriptors, the color and edge directivity descriptors, the color layout descriptors, and the scalable color descriptors, are extracted for robust detection. We employ the local binary fitting model as the statistical learning model to update by the input videos in real time. We performed experiments on the publicly available UCSD anomaly detection dataset and showed that our method has good performance for detecting and localizing abnormality compared to the state-of-the-art methods.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 31201133).

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Correspondence to Liu Kangwei.

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Kangwei, L., Jianhua, W. & Zhongzhi, H. Abnormal event detection and localization using level set based on hybrid features. SIViP 12, 255–261 (2018). https://doi.org/10.1007/s11760-017-1153-0

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  • DOI: https://doi.org/10.1007/s11760-017-1153-0

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