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Fast Anomaly Detection Based on 3D Integral Images

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Abstract

In this paper, we propose a method to detect abnormal events from videos based on the integral image under Bayesian framework. In our implementation, we consider a regular cube in the videos as one event. Each event is represented as a motion histogram which can be calculated fast from our proposed 3D integral images. Furthermore, we estimate the anomaly probability under the Bayesian framework, where we estimate the prior knowledge from the motion magnitudes and calculate the likelihood based on our maximum histogram templates. Experiments on the public datasets show that our method can effectively and efficiently detect abnormal events in complex scenes.

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Notes

  1. We keep two significant digits because that the compared methods from different references also have the same significant digits.

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Acknowledgements

This work was jointly supported by the National Natural Science Foundation of China (61402049) and Science and Technology Research Project of the Department of Education of Liaoning Province (LJKZ1019).

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  1. Bohai University, Jinzhou, China

    Shifeng Li, Yan Cheng, Yunfeng Liu & Yuqiang Yang

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  1. Shifeng Li
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Correspondence to Shifeng Li.

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Li, S., Cheng, Y., Liu, Y. et al. Fast Anomaly Detection Based on 3D Integral Images. Neural Process Lett 54, 1465–1479 (2022). https://doi.org/10.1007/s11063-021-10691-8

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