Centralized analytics
Centralize multi-channel AI video analytics for larger CCTV deployments and integration projects.
Published reference price: CNY 45,000. The final quotation defines the exact GPU, storage, software scope, warranty, delivery and taxes.
A server intended to run a multimodal model needs a separate VRAM and event-throughput check. Do not assume that the full video-channel rating remains available alongside every large-model workload.
The AI Video Analytics Server is suitable for larger deployments where a central server is preferred over distributed edge devices. It can receive multiple streams, run AI models, and push structured events into third-party systems.
The right configuration depends on the customer’s camera count, video resolution, FPS, selected algorithms, and latency expectations.
Specifications
Multi-channel video analytics
A video analytics server is normally considered when a project has more camera streams, a central equipment room, and a requirement to manage local inference and event output across several areas. It should be sized from the intended workload rather than from camera count alone.
Separate high-priority live alarms from lower-frequency inspection tasks. A busy gate, loading area, production line, or conveyor can require more frequent analysis than a quiet perimeter camera. Grouping streams in this way produces a more realistic capacity plan than treating every channel as identical.
Confirm how streams reach the server, whether cameras and NVRs expose usable substreams, and what happens when links are interrupted. Codec, bitrate, frame rate, video resolution, and simultaneous decoding influence practical throughput alongside the selected model and AI processing resources.
Large deployments benefit from consistent camera names, location codes, algorithm names, region identifiers, and event fields. These details help a VMS, security console, or business platform identify which rule raised an event and where the underlying evidence can be retrieved.
A commissioning test should include the expected peak stream count, live alarm generation, event search, external delivery, and a restart or camera-reconnect scenario. The project team can then document the working stream mix and reserve headroom for later additions.
Related planning: Guide: accepting an AI video analytics project. For fewer distributed camera points, a local AI camera or edge box may reduce the amount of central equipment required.
The Chinese demonstration shows local video recognition, captured images and alarm records. Available functions depend on the delivered software and configuration.
The Chinese demonstration shows camera access, algorithm configuration, monitoring and event review. Available functions depend on the delivered software and configuration.
Project inquiry
Tell us the camera count, the events to detect and the project location. Our team will reply by email with the relevant product information and next steps.