Comparative analysis of video anomaly detection algorithms

Xianggui Cheng; Lining Yuan; Zhao Liu; Fang Guo

doi:10.1117/12.2641049

2 December 2022 Comparative analysis of video anomaly detection algorithms

Xianggui Cheng, Lining Yuan, Zhao Liu, Fang Guo

Proceedings Volume 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022); 122881R (2022) https://doi.org/10.1117/12.2641049
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 2022, Zhuhai, China

Abstract

With the continuous development of deep learning, anomaly detection technology in computer vision has maderemarkable progress. Video anomaly detection is essential to ensure public safety. We classify and summarize anomalydetection based on deep learning. First, the overall process of anomaly detection is presented. Then, based on the neural network training method, we discuss the development and application of deep learning in the field of anomaly detectionfrom four aspects: Multiple Instance Learning, Regression models, Clustering models, and Reconstruction models. Finally, we present commonly used datasets and performance evaluation criteria , analyze the performance of different algorithms and discuss the future directions.

Citation Download Citation

Xianggui Cheng, Lining Yuan, Zhao Liu, and Fang Guo "Comparative analysis of video anomaly detection algorithms", Proc. SPIE 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 122881R (2 December 2022); https://doi.org/10.1117/12.2641049

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KEYWORDS

Video

Video surveillance

Detection and tracking algorithms

RGB color model

Algorithm development

Optical flow

Feature extraction

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