rekkles2/Fed_WSVAD

[IEEE TII 2025] Official Implementation for "Dual-Detector Reoptimization for Federated Weakly Supervised Video Anomaly Detection via Adaptive Dynamic Recursive Mapping"

33
/ 100
Emerging

This project helps operations engineers and surveillance managers automatically spot unusual events in video feeds while protecting privacy. It takes raw video data from multiple locations and identifies anomalies, even with limited initial labeling, providing scores that indicate the likelihood of an unusual event. This is ideal for those managing distributed video surveillance systems who need to detect anomalies efficiently.

Use this if you need to detect unusual activities in video surveillance across many locations without centralizing all your sensitive video data.

Not ideal if you require real-time, instantaneous anomaly detection or if you can freely share and centralize all your video data for traditional analysis.

video-surveillance anomaly-detection privacy-preserving-AI industrial-monitoring distributed-operations
No Package No Dependents
Maintenance 6 / 25
Adoption 7 / 25
Maturity 16 / 25
Community 4 / 25

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Stars

26

Forks

1

Language

Python

License

Apache-2.0

Last pushed

Nov 11, 2025

Commits (30d)

0

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