Published: 2026-07-28 | English reference version of the Chinese source article.
Where an AI Edge Box sits in an existing CCTV system
Many projects already have cameras, switches, an NVR and a management platform. They still depend on recordings and someone watching screens. An AI Edge Box can take camera or NVR streams, decode them locally, run the model, apply the scene rule, capture evidence and send the event to the platform.
The camera continues to create the image and the NVR continues to record it. The edge device performs video analysis. This lets an existing CCTV system add targeted AI functions without replacing every camera.
The work behind a usable alarm
The pipeline includes stream access, decoding, image preparation, model inference, target tracking, rule evaluation, event deduplication, capture storage and interface delivery. A fault in any part of that path can make an alarm unusable.
Stream access means more than recognizing RTSP, ONVIF or GB28181. The team checks account rights, routing, main or sub stream, H.264 or H.265, resolution, bitrate, concurrent stream capacity and recovery after a loss of video. The inference model also needs to suit the CPU, NPU or GPU used on the delivered device.
Why TOPS does not answer the channel question
TOPS measures theoretical compute at a stated numeric precision. It does not state how many camera channels will run steadily in a project. Resolution, decoder format, model structure, algorithms per stream, analysis frame rate, target density, live preview and event delivery all add to the load.
A project should make a load sheet for every channel: resolution, codec, task, planned processing rate, preview need, capture need and platform push need. The intended combination then runs continuously on the intended hardware. Capacity should include room for stream reconnects, busy periods and centralized preview.
Algorithms need a camera view that can support them
A helmet task needs a clear head outline. A reflective-vest task needs upper-body detail. Smoking and phone-use tasks need the relation between a hand and head to remain visible. Plate recognition depends on character pixels, vehicle angle, speed and exposure.
Real-site testing should preserve true events, ordinary activity and confusing negatives. Night video, backlight, steam, dust, glare, vibration, dirty lenses, webbing and partial occlusion affect the input directly. Processing cannot recreate a detail that never reached the camera sensor.
Acceptance follows the whole event path
Acceptance checks video access, recognition, rule judgement, evidence capture, event delivery and recovery together. It includes the planned streams and rules, day and night samples, real site trials, false and missed event records, continuous load, HTTP or MQTT field checks, and recovery after stream loss, network loss, service failure or a restart.
Accuracy also needs an agreed target definition, exclusions, test period, sample set, count method and acceptable boundary. AI video analytics supports operators. It does not replace fire protection, access control, interlocks, site inspection or statutory safety responsibilities.