Data sources
Video streams, camera feeds, object detection datasets, movement patterns, event logs, zone-based activity data, and operational alert history.
AI Surveillance System for Real-Time Threat Detection
An intelligent AI surveillance framework designed to detect objects, activities, anomalies, and potential threats using real-time computer vision and edge AI deployment.
Programme status
Applied research programme
Research cycle
2026
Technical methods
6 focus areas
Evaluation
4 planned dimensions
Research overview
From operational problem to testable research programme
Modern surveillance systems generate large volumes of video data, but most monitoring still depends on manual observation. This creates delays, missed incidents, and limited situational awareness.
Traditional CCTV and monitoring systems are passive. They record events but do not understand what is happening, detect abnormal activity, or generate intelligent alerts in real time.
Intended research impact
Real-time detection, tracking, and anomaly alerts for mission-critical environments.
Programme objectives
What this research is designed to establish
- Create a configurable event taxonomy for people, vehicles, objects, zones, and operational risk conditions.
- Study how detection, tracking, temporal context, and site rules can be combined without creating excessive alerts.
- Compare edge, on-premises, and cloud inference patterns for latency, resilience, and operational maintainability.
Reference architecture
A traceable path from source data to reviewed output
01
Video input
02
Vision AI detection
03
Tracking engine
04
Anomaly detection
05
Risk scoring
06
Alert dashboard.
Computer VisionObject DetectionMulti-Object TrackingAnomaly DetectionEdge AI DeploymentReal-Time Alerting
Research questions
Questions guiding the investigation
- Which visual and temporal signals are reliable enough to support a human-reviewed threat alert?
- How do missed detections and identity switches affect downstream anomaly and risk scoring?
- How should thresholds adapt across lighting, weather, camera angle, crowd density, and site-specific operating rules?
Evaluation plan
How the programme will be assessed
- Measure detection precision and recall by object class, scene type, and operating condition.
- Review tracking continuity, event latency, and resource usage on representative edge hardware.
- Run operator-led false-alert reviews and document the reason for each accepted or rejected event.
- Test degraded-network, camera-loss, and recovery scenarios before operational deployment.
Methodology
We use object detection, multi-object tracking, behavior recognition, anomaly detection, and rule-based risk scoring to identify suspicious activity and trigger real-time alerts.
Planned outputs
Evidence and assets the programme should produce
- Documented event taxonomy and annotation guidance.
- Reference edge-to-dashboard architecture and alert workflow.
- Evaluation report covering accuracy, latency, reliability, and operator review.
- Prototype dashboard for evidence, acknowledgement, escalation, and audit history.
Deployment context
Designed for deployment across surveillance control rooms, smart facilities, warehouses, industrial sites, forest zones, and distributed monitoring operations.
Intended outcome
Improved real-time incident awareness by converting passive camera feeds into intelligent detection, tracking, and alert systems.
Real-world application
Applicable for security surveillance, forest monitoring, restricted zone detection, perimeter monitoring, and mission-critical operations.
Scalability
Supports multi-camera expansion, cloud dashboard integration, edge inference, and centralized monitoring.
Future work
Integration with multimodal signals such as thermal cameras, audio alerts, drones, and geospatial intelligence.
Ethics and responsible AI
Built with responsible AI principles, access control, privacy-aware deployment, and clear operational boundaries.
Known limitations
Evidence must define the operating boundary
Performance depends on camera placement, image quality, environmental conditions, event definitions, and the representativeness of evaluation data. Alerts are decision-support signals and require an approved human response workflow.
Conclusion
AI surveillance transforms monitoring from passive recording into real-time intelligence and faster response.
This page describes a research programme and evaluation approach. It does not claim a published paper, certified system, completed benchmark, patent, partnership, or guaranteed operational result.