On-prem · Edge AI · No cloud required
r450k discovers your ONVIF cameras, detects what matters with on-device AI, records continuously, and streams live to the browser. It scales with your hardware — from a single Raspberry Pi watching a couple of cameras to a GPU server running real-time AI across dozens. Your footage never leaves your network.
Entry tier runs on a Raspberry Pi 4 (a couple of cameras with motion or nano-model AI). Real-time, multi-camera detection and teaching the camera your own skills scale up to mini-PCs and NVIDIA GPUs.
Capabilities
Detection, recording, live view, security, and fleet management — built to run on small devices, entirely on your own network.
Two-axis rules pair a mode — presence, crowd, intrusion, line crossing, multi-line crossing, or LPR — with target classes from a data-driven registry, scoped to multiple detection zones per rule. Fire and smoke ship as first-class event classes, and an "Anything" wildcard fires on any detected object.
A second-stage plate detector + OCR on a dedicated high-res frame fires on any readable plate or only a watchlist. Fuzzy matching absorbs OCR errors; plate text, vehicle type, and color ride the alert metadata.
Enroll the people you know, and a detection becomes a named alert. On the edge node, faces are aligned, embedded, and matched against your private gallery — so “person detected” turns into “a known face at the front door.” Enrollment and matching run entirely on-prem: no cloud service, no faces ever leaving your network.
Rolling segment buffer with event-triggered MP4 clip extraction, plus a low-resource JPEG ring-buffer mode. Optional one-pass GPU H.265/H.264 re-encode shrinks footage with on-the-fly playback transcode for any browser.
Direct H.264 RTP-to-WebRTC live view with MJPEG fallback and live sound on any camera codec (G.711 pass-through, AAC→Opus transcoding). Two-way audio talk-back and press-and-hold PTZ let operators respond, not just watch — across paged grids from 1×1 to 4×4.
Recordings, snapshots, and training images are AES-256-GCM encrypted on disk; factory reset crypto-erases by destroying the key and multi-pass shreds deleted footage. The key is wrapped by an OS keystore (DPAPI / systemd-creds) or a portable passphrase with an exportable recovery escrow — and a passphrase-encrypted .mmbackup moves cameras, rules, and settings between hosts.
No ML jargon, no dataset wrangling. A guided wizard teaches the camera a new skill — name it, pick a goal (recognize a new object, tell good from bad, or spot anything unusual), show a few examples, check the accuracy, and turn it on. It builds and trains a real custom model behind the scenes, then hot-swaps the live detector.
One feed across AI detection, camera and machine health, and login security — with per-event acknowledge, annotated screenshots, and in-page clip playback. Filter the feed down to a single source camera to isolate one view’s events. Route to webhook, Telegram, or MQTT.
Authenticated UDP multicast discovery + single-parent adoption with a short-lived claim code. Nodes enroll for a fleet-CA certificate and serve a mutual-TLS management channel — no inbound ports, no cloud broker. Each node’s certificate auto-renews on an operator-gated schedule, so fleet trust stays current and no forgotten node lapses.
The control plane puts your whole estate on a map — sites and buildings pinned by location, with every edge node and its cameras layered on top. Drop into a building to design its floor plan in-app, place each camera on the plan with a field-of-view coverage cone, and see one building’s cameras aggregated across several nodes. Click any camera to locate it or open its live view straight from the map.
The unified event feed becomes insight — KPI tiles, detections over time, and per-category, per-camera breakdowns with a range selector and auto-refresh. It goes past charts: an activity heatmap by hour and weekday, an expected-range band that flags when a camera breaks its own normal pattern, spike and "unusual silence" anomaly alerts, a per-camera reliability scorecard, and a notification noise ratio. Aggregation runs server-side, so it works on SQLite or a full database engine.
Search your footage by what the cameras actually saw. Every detection is coalesced onto a searchable timeline — filter by camera, date range, one or more object types at once, and confidence, then jump straight to the exact moment in the recording, with the object boxed on a preview thumbnail. Export the results to CSV or PDF. It taps the live detector's own output, so there is no second video pass.
A first-run wizard walks setup end to end, a capacity estimator sizes your host, and machine-health monitoring self-heals — overwriting the oldest footage before the disk fills. ffmpeg, the Python AI runtime, and app updates all install from inside the app.
How it works
Authenticated ONVIF discovery and manual probe find cameras on your LAN; saved devices persist in a local SQLite database.
On-device YOLO inference runs your detection rules — presence, crowd, intrusion, line crossing, or plate recognition — against the live frames.
Continuous NVR recording rolls in the background and extracts an MP4 clip the moment a rule fires, encrypted on disk.
Alerts land in the unified feed with an annotated snapshot and clip, and fan out to webhook, Telegram, or MQTT destinations you choose.
Hardware
The same app runs from a pocket-sized appliance to a GPU server — you choose the tier. (Camera counts are rough; the built-in capacity estimator measures the real number for your host.)
Raspberry Pi 4/5 · 4 cores · 2–4 GB
~1–4 cameras
Live view + recording for a couple of cameras; AI is native motion or YOLO-nano at a relaxed interval. SQLite, CPU software decode, no GPU.
Mini-PC / NUC · 6–8 cores · 8–16 GB
~6–16 cameras
Continuous recording with real-time YOLO detection at the 2 s default. An entry NVIDIA GPU lifts camera count and keeps AI real-time.
Server · 12+ cores · 32 GB+ · NVIDIA GPU
20+ cameras
GPU-accelerated detection, in-app model training, HEVC NVENC compression, and long retention. Server DB engine + Redis at scale.
See it in action
Live multi-camera grids, detection rules drawn on the real frame, guided camera teaching with no ML jargon, continuous NVR recordings, event analytics, encrypted backups, and version & health — every screen in the browser, in four languages.
Use cases
Train a custom model to flag product defects, missing parts, or PPE gaps on the line — private, on-prem, with no per-seat AI fees.
Crowd and intrusion alerts, plus license-plate recognition for forecourt and drive-through monitoring.
Watch loading docks and yards — vehicle and person detection with line-crossing at gates, all on edge hardware.
After-hours intrusion detection with secure, encrypted recording that stays on-site.
Spot animals or intruders across land with poor or no connectivity; detection and recording run entirely locally.
After-hours movement and fall-style alerts with footage that never leaves the building — privacy by default.
Zone and line-crossing rules for restricted areas and equipment, on rugged hardware where uplinks are unreliable.
A control plane adopts many edge nodes over the LAN, maps every site and building, and relays live view back to operators — with per-building floor plans that place each camera and its coverage.
The platform
r450k is a modular platform. mymatasan is the edge camera node and myiotsan is the edge sensor hub; the control plane adopts both and correlates across them, and shared identity ties it together.
Flagship · in active development
The standalone edge camera & video-intelligence node: ONVIF, AI detection, NVR, WebRTC live view, encryption, and LAN pairing.
Sensor hub · available now
The NVR, but for sensors: door contacts, motion, temperature, smoke, leaks, power and access readers over an embedded MQTT broker — with rules, alerts, safe actuation, and telemetry history.
Control plane · available now
The fleet control plane that discovers, adopts, and manages both camera and sensor nodes — mapping every site, building, and node with per-building floor plans and camera coverage — and correlates events across them: motion on a camera AND a door opening AND no badge swipe.
Identity · available now
The single sign-on front door: one federated identity across every app, with local accounts, enterprise LDAP / Active Directory, Kerberos SPNEGO desktop SSO, and generic OIDC providers — plus group-to-role mapping and per-app role-based access control. A single pure-Go binary that runs fully on your intranet, no egress.
Pricing
Personal and non-commercial use is free — forever. Businesses keep the project alive with a simple commercial license. This is early pricing; we’ll tune it together as things grow.
Prices in USD. Non-commercial use stays free. Not sure which fits? Reach out and we’ll figure it out together.
Get the apps
Run one node at a single site, or a whole fleet from one control plane. Both are a single install with the web UI and a default config bundled.
Free for personal and non-commercial use — individuals, non-profits, education, and research. Commercial or in-business use, and any reselling, needs a commercial license.
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r450k brings cloud-grade camera AI to hardware you already own, with privacy as the default rather than an upgrade.
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