fudanyliu/GVAED

Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models

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This project helps security analysts, operations managers, and urban planners automatically identify unusual or suspicious events in surveillance footage. It takes raw video feeds as input and outputs flagged segments or alerts when it detects anomalies like unusual crowd behavior, unauthorized entry, or other deviations from normal patterns. This is ideal for anyone who monitors large numbers of video feeds and needs to quickly spot abnormal activities without constant manual observation.

No commits in the last 6 months.

Use this if you need to systematically evaluate and compare different deep learning models for detecting anomalous events in video surveillance.

Not ideal if you are looking for a ready-to-deploy application for real-time anomaly detection without any development work.

video-surveillance security-monitoring crowd-management public-safety operations-analytics
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 8 / 25
Maturity 16 / 25
Community 13 / 25

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44

Forks

6

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License

MIT

Last pushed

Mar 27, 2024

Commits (30d)

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