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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Unsupervised approaches for video anomaly detection may not perform as goodas supervised approaches. However, learning unknown types of anomalies using anunsupervised approach is more practical than a supervised approach asannotation is an extra burden. In this paper, we use isolation tree-basedunsupervised clustering to partition the deep feature space of the videosegments. The RGB- stream generates a pseudo anomaly score and the flow streamgenerates a pseudo dynamicity score of a video segment. These scores are thenfused using a majority voting scheme to generate preliminary bags of positiveand negative segments. However, these bags may not be accurate as the scoresare generated only using the current segment which does not represent theglobal behavior of a typical anomalous event. We then use a refinement strategybased on a cross-branch feed-forward network designed using a popular I3Dnetwork to refine both scores. The bags are then refined through a segmentre-mapping strategy. The intuition of adding the dynamicity score of a segmentwith the anomaly score is to enhance the quality of the evidence. The methodhas been evaluated on three popular video anomaly datasets, i.e., UCF-Crime,CCTV-Fights, and UBI-Fights. Experimental results reveal that the proposedframework achieves competitive accuracy as compared to the state-of-the-artvideo anomaly detection methods.

DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Network