pipixin321/GlanceVAD
[ICME 2025 Oral] Official implementation of "GlanceVAD: Exploring Glance Supervision for Label-efficient Video Anomaly Detection"
This project helps security analysts or operations managers improve the accuracy of automatically detecting unusual events in surveillance footage. By providing a single timestamp for an anomalous moment within each flagged incident, it trains a system to identify these events more reliably. The input is video data (or features derived from it), and the output is a system that can flag abnormal activities with fewer false alarms than previous methods.
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Use this if you need to build a more accurate video anomaly detection system for surveillance or security footage, and you can provide a single 'glance' timestamp for each abnormal event you want the system to learn from.
Not ideal if you cannot provide any labels for abnormal events, or if you need to detect anomalies in domains other than video, such as sensor data or network traffic.
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34
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2
Language
Python
License
MIT
Category
Last pushed
Mar 23, 2025
Commits (30d)
0
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