Video anomaly detection via improved future frame prediction

Bo Li; Zhongxin Li; Zefeng Yin

doi:10.1117/12.3021297

19 February 2024 Video anomaly detection via improved future frame prediction

Bo Li, Zhongxin Li, Zefeng Yin

Proceedings Volume 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023); 130630I (2024) https://doi.org/10.1117/12.3021297
Event: Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 2023, Changchun, China

Abstract

Video anomaly detection is extensively utilized across a variety of domains including public transportation, industrial production, city management, and military fields to mitigate risks and bolster enhance safety. To tackle the challenges associated with video anomaly detection in intricate environments, we propose a light but efficient framework that builds upon future frame prediction techniques. Our framework incorporates Convolutional Long Short-Term Memory (ConvLSTM), masked convolution, and attention mechanisms to enhance the detection accuracy. Furthermore, to simplify the model's complexity, we replace the convolutional layers in the network with depthwise separable convolutions (DSC). Through evaluation on public datasets such as CUHK Avenue, UCSD Peds1, and UCSD Peds2, our proposed network model exhibits both high accuracy and real-time performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bo Li, Zhongxin Li, and Zefeng Yin "Video anomaly detection via improved future frame prediction", Proc. SPIE 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 130630I (19 February 2024); https://doi.org/10.1117/12.3021297

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