How is existing CCTV used for AI analytics?
Most industrial sites already have IP cameras recording to an NVR or VMS for security. Those cameras expose video streams (typically via RTSP, often discoverable via ONVIF). An analytics system reads the stream, runs detection models on each frame, applies rules (zones, schedules, durations) and outputs events. The cameras and recording system stay as they are.
Not every view is suitable for every use case. A camera that is fine for detecting a person in a restricted zone may be too far away to check safety glasses. That is why camera assessment comes before any commitment — see how to evaluate cameras for computer vision.
Edge vs on-premise vs cloud
| Edge / on-premise | Cloud | Hybrid | |
|---|---|---|---|
| Where video is processed | On hardware inside the site network | In a cloud environment | On-site |
| Video leaves the site | No | Yes | No (events only) |
| Bandwidth need | Low | High (continuous upload) | Low |
| Typical fit | Plants, banks, sensitive sites | Small, distributed sites | Multi-site organisations |
For most industrial projects, on-premise processing is the default because it keeps video inside the network and works with limited internet bandwidth. More detail: edge vs cloud video analytics.
Typical manufacturing use cases
Workplace safety
PPE detection, restricted area monitoring, forklift–pedestrian detection, fall detection and fire and smoke detection give HSE teams continuous coverage instead of periodic walk-arounds.
Quality inspection
Visual quality inspection detects visible defects at line speed. Unlike safety use cases, it usually needs dedicated cameras and lighting at the inspection point.
Production and operational analytics
Production line monitoring, product counting and workstation analytics measure running time, micro-stops, output and bottlenecks — including on older machines without PLC data.
Camera requirements
- Stream access: RTSP/ONVIF from the camera or NVR.
- Resolution and distance: the subject must cover enough pixels; 1080p is usually enough for people-level events at moderate distance.
- Angle: angled views from above work better than strict top-down views for PPE and posture.
- Lighting: stable lighting or good IR for night operation; strong backlight is a common problem.
- Frame rate: fast processes (vehicles, conveyors) need higher frame rates.
Deployment process and integration
A typical project moves from discovery and camera assessment to a defined pilot, validation on your own footage, and a rollout decision. Events are delivered to a dashboard and notifications, and can be sent to other systems via REST API/webhooks or local relay outputs. See how a pilot works.
Privacy
Industrial safety and operations analytics do not need to identify people. Hype Vision does not use face recognition for these use cases. With on-premise processing, video stays inside your network, and retention and access rules follow your GDPR/KVKK policies.
Limitations worth knowing
- Computer vision only sees what the camera sees; occlusion, distance and lighting set the limits.
- Camera-based monitoring is not a substitute for safety-rated machine guarding or certified fire detection.
- Performance varies per camera and must be measured on site, not quoted from a datasheet.
Where Hype Vision fits
Hype Vision develops computer-vision and AI video-analytics solutions that run on existing IP/CCTV cameras for industrial safety, manufacturing, quality and operational monitoring. The team is based at GTÜ Teknopark in Gebze, Türkiye, and has been working on industrial AI projects since 2020.
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