Edge Computing
Edge computing processes data on or near the device that generated it — for video analytics, that means running AI inference on the camera itself or on a local server rather than sending streams to the cloud.
How It Works
In an edge video analytics deployment:
- Cameras (or a nearby edge server) run trained AI models locally on specialized hardware — GPUs, NPUs, or VPUs.
- Only metadata — detections, alerts, snapshots — is sent over the network.
- Raw video stays on-site unless a specific incident requires escalation.
This architecture trades some flexibility (harder to retrain) for major gains in latency, bandwidth, privacy, and resilience.
Why It Matters
Cloud-only video AI has three structural problems that edge solves:
- Latency. Streaming 1080p H.265 to the cloud, running inference, and returning an alert adds hundreds of milliseconds to seconds. Edge inference runs in under 50 ms.
- Bandwidth. A single 4 Mbps camera uses ~43 GB/day. 500 cameras uploaded 24/7 is not cost-effective.
- Privacy and sovereignty. Many jurisdictions prohibit shipping biometric video outside national borders.
- Time-critical alerts — gun detection, fall detection, smoke/fire where seconds matter
- Bandwidth-constrained sites — remote industrial, maritime, or mining locations
- GDPR/HIPAA environments — biometric or medical footage must stay on-premise
- Resilient deployments — analytics must keep working during network outages
- Retail chains — hundreds of stores without per-store cloud cost
IncoreSoft's VEZHA platform supports edge, cloud, and hybrid deployments, letting each site choose the right balance.
Use Cases
Frequently Asked Questions
What's the difference between edge and cloud video analytics?
Cloud runs AI in centralized data centers — flexible and easy to update, but higher latency and bandwidth cost. Edge runs AI on-site — faster and private, but requires capable local hardware.
Can edge analytics run on existing cameras?
Some modern cameras ship with on-board AI chips (e.g., Axis ARTPEC-8). For older cameras, IncoreSoft deploys an edge server or appliance that connects over RTSP and runs analytics locally.
How do edge deployments handle model updates?
A hybrid architecture is common: models are trained in the cloud and pushed to edge devices over secure channels on a scheduled basis, combining cloud flexibility with edge performance.
Read also
Face Recognition
Face recognition is a biometric technology that identifies or verifies a person by extracting mathematical features from their facial image and comparing those features against a reference database.
Deep Learning
Deep learning is a branch of machine learning that uses multi-layered neural networks to learn patterns directly from raw data — images, video, text, audio — without hand-crafted rules or features.
Computer Vision
Computer vision is the field of artificial intelligence that enables machines to extract meaningful information from images and video — effectively giving software the ability to "see" and interpret the visual world.
Ready to Get Started?
Fill in the form and our team will get back to you shortly.