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International Journal of Multimedia Information Retrieval
Erschienen in: International Journal of Multimedia Information Retrieval 1/2023

01.06.2023 | Regular Paper

Video anomaly detection with memory-guided multilevel embedding

verfasst von: Liuping Zhou, Jing Yang

Erschienen in: International Journal of Multimedia Information Retrieval | Ausgabe 1/2023

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Abstract

Playing a vitally important role in the operation of intelligent video surveillance system and smart city, video anomaly detection (VAD) has been widely practiced and studied in both industrial circles and academia. In the present study, a new anomaly detection method is proposed for multi-level memory embedding. According to the novel method, the feature prototype of the sample is stored in the memory pool, which enhances the diversity of the sample feature prototype paradigm. Besides, the memory is embedded in the decoder in a hierarchical integrating manner, which makes the feature information of the object more complete and improves the quality of features. At the end of the model, modeling is performed for the channel relationship between the features of the object in the channel dimension, thus making the model capable of more efficient anomaly detection. This method is verified by conducting evaluation on three publicly available datasets: UCSD Ped2, CUHK Avenue, ShanghaiTech.
Vorheriger Artikel Nested-Net: a deep nested network for background subtraction
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Metadaten
Titel
Video anomaly detection with memory-guided multilevel embedding
verfasst von
Liuping Zhou
Jing Yang
Publikationsdatum
01.06.2023
Verlag
Springer London
Erschienen in
International Journal of Multimedia Information Retrieval / Ausgabe 1/2023
Print ISSN: 2192-6611
Elektronische ISSN: 2192-662X
DOI
https://doi.org/10.1007/s13735-023-00272-x

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