English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In contemporary society, surveillance anomaly detection, i.e., spottinganomalous events such as crimes or accidents in surveillance videos, is acritical task. As anomalies occur rarely, most training data consists ofunlabeled videos without anomalous events, which makes the task challenging.Most existing methods use an autoencoder (AE) to learn to reconstruct normalvideos; they then detect anomalies based on their failure to reconstruct theappearance of abnormal scenes. However, because anomalies are distinguished byappearance as well as motion, many previous approaches have explicitlyseparated appearance and motion information-for example, using a pre-trainedoptical flow model. This explicit separation restricts reciprocalrepresentation capabilities between two types of information. In contrast, wepropose an implicit two-path AE (ITAE), a structure in which two encodersimplicitly model appearance and motion features, along with a single decoderthat combines them to learn normal video patterns. For the complex distributionof normal scenes, we suggest normal density estimation of ITAE features throughnormalizing flow (NF)-based generative models to learn the tractablelikelihoods and identify anomalies using out of distribution detection. NFmodels intensify ITAE performance by learning normality through implicitlylearned features. Finally, we demonstrate the effectiveness of ITAE and itsfeature distribution modeling on six benchmarks, including databases thatcontain various anomalies in real-world scenarios.

Unsupervised Video Anomaly Detection via Normalizing Flows with Implicit Latent Features