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Use case
CCTV AI datasets for threat detection and monitoring
Security AI systems detect intrusions, loitering, PPE violations, and vehicle events across thousands of camera feeds. We label with privacy-aware guidelines and high recall targets.
Industry challenges
- !Low-resolution night footage
- !False positive control for alert systems
- !Restricted zone polygon complexity
How we help
- Person, vehicle, and zone polygon labels
- Behavior and event tagging on clips
- 24/7 labeling ops for continuous ingest
Annotation types
Bounding boxesZonesVideo eventsRe-ID tracks
Security & Surveillance data annotation services
Enterprise security AI monitors thousands of cameras for intrusions, loitering, PPE violations, and vehicle events. Models fail without diverse, accurately labeled footage from real deployments.
We annotate persons, vehicles, zones, and behaviors with privacy-aware guidelines and high-recall targets suitable for alert systems.
Scale security video annotation without sacrificing precision — our 24/7 ops match continuous camera ingest.
Key benefits
- ✓Person and vehicle detection at scale
- ✓Restricted zone polygons
- ✓Event and behavior tags on clips
- ✓Night and low-resolution footage expertise
Best practices for security & surveillance labeling
- Define alert-triggering classes clearly
- Balance recall and precision in QA sampling
- Document false-positive review criteria
Frequently asked questions
- What security footage can you annotate?
- CCTV, IP cameras, and body-worn clips for person detection, zone intrusion, and activity recognition.
- How do you reduce false positives in labels?
- Multi-pass QA, consensus on ambiguous frames, and guidelines tuned to your alert thresholds.