List of Features and Technical Specifications Category Specification System Type Home Security System + Network Video Recorder (NVR) Processor ArmSoM Sige7 (RK3588), 8-core ARM CPU NPU 6 TOPS for YOLO object detection, license plate recognition (OCR), face recognition GPU/VPU Hardware VPU for H.264/H.265, decoding up to 32 channels of 1080p@30fps Memory 8GB LPDDR4 Storage M.2 SSD slot (1TB local storage installed) Networking Dual 2.5GbE ports (camera LAN isolation from home LAN) Power USB-C, idle 2.5W / full load ~10W (15W budget) Cooling Passive, target thermal stability at 65°C (ambient 45°C) Software Stack Ubuntu + Docker + Frigate NVR + Home Assistant + MQTT + ffmpeg Camera Support 8 cameras × 2 streams (1080p@25fps recording + 640×480@5fps detection) Automation Home Assistant (WhatsApp, AI agent, smart device control) Remote Access Mobile video/snapshot review, local + cloud archive Project Background This system is deployed in a large family home in Sydney, Australia. The goal was to build a low-power, high-performance NVR running 24/7. Beyond basic security monitoring and logging, the system needs to: Recognize people, cars, motorcycles, and cats, and trigger different automations based on the scenario Detect garage door open/closed status and lights on/off via image recognition (no traditional sensors available in the old house) Send snapshots of people/cars/motorcycles to an external AI agent to identify mail or package delivery, then push WhatsApp notifications Feed cat detection images to an external Home Automation system to catalog and store to enable cat finder utility The system must integrate with Home Assistant to access AI agents, WhatsApp gateway, and smart device controllers. Local and cloud archive and mobile review are also required. Challenges Faced 1. Reliable image recognition under extreme lighting changes Garage door status recognition faces full daylight sun to nighttime darkness. Multiple rounds of image training were required. 2. License plate recognition (LPR) under harsh conditions LPR must run in real-time across day, night, and rainstorms using NPU-based OCR. When the homeowner drives up the driveway, the time from positive LPR to garage door/lights activation must be under 2 seconds with very high accuracy. 3. Thermal constraints Sydney summer temperatures often exceed 40°C, reaching up to 45°C. With a thermal budget of ambient +20°C, the system must be stable at 65°C with a safety buffer at 70°C. 4. Performance vs. power balance 8 cameras continuously streaming recording (1080p@25fps) + detection (640×480@5fps) Dual network ports for camera/home LAN isolation Total power budget ≤15W Silent operation, low heat output, high stability 5. Open-source software ecosystem Must use industry-standard processor with full open-source support (Linux, Docker, Frigate, etc.) Screenshot of RKTOP performance monitor for RK3588. CPU, GPU, NPU just idling and temperature at 49 degrees C. The Sige7 Solution (Why ArmSoM Sige7?) The Sige7 board (RK3588) is one of the few platforms that meets all key requirements simultaneously: Requirement How Sige7 Meets It Low power Idle 2.5W, full load ~10W (fits 15W budget) High-performance CPU 8-core ARM, ~2× performance of Raspberry Pi 5 Video processing Hardware VPU decodes 32×1080p@30fps; ffmpeg hardware transcoding NPU acceleration 6 TOPS for YOLO, LPR (OCR), face recognition – offloads CPU/GPU Sufficient memory 8GB LPDDR4 fits OS, cache, Frigate system, and video frame buffers Dual 2.5GbE Physical isolation between camera network and home LAN M.2 SSD 1TB local storage for smooth video scrubbing Open-source support Optimized Ubuntu with stable Rockchip drivers; runs Frigate + Home Assistant perfectly Thermal stability CPU temperature ~49°C (ambient 45°C), well below 65°C target Implementation Process Software Stack – Frigate NVR Leverages Sige7’s heterogeneous computing: Video decoding/transcoding – ffmpeg with GPU/VPU hardware acceleration (H.264/H.265) Storage optimization – Direct-to-disk recording + RAM cache + segment writing to reduce SSD wear Motion detection – CPU handles lightweight motion detection and audio transcoding Object detection & recognition – NPU runs YOLO for detection and bounding boxes; if recognition is needed (face, LPR, subclass like "delivery truck"), NPU handles that as well License plate recognition – Both plate detection and OCR run on NPU (saving GPU/CPU cycles). Driveway camera runs at 1080p@5fps for better accuracy and range. Automation Integration Frigate sends events via MQTT to Home Assistant, which handles: External AI analysis (e.g., is the person a mail carrier or a passerby?) WhatsApp messaging (with AI summary + snapshot) Smart device control (garage door, lights) Frigate Metrics view Network Deployment Dual 2.5GbE ports – one for home LAN, one dedicated to camera subnet for security and traffic isolation. Thermal Validation Tested in Sydney summer conditions. Passive cooling keeps CPU temperature steady at ~49°C, well within the 65°C target. Outcomes and Benefits Measured Performance Metric Value Uptime 33 days (and growing) Average CPU load ~25% across 8 cores CPU temperature ~49°C Object detection speed ~58ms average NPU load <20% (near zero during low motion) SSD usage (33 days) 298GB / 1024GB (includes OS, Docker, Frigate, tools) Power draw Idle 2–3W / peak 8W Achieved Outcomes ✅ LPR → garage door/lights in <2 seconds, high accuracy across day/night/rain ✅ Real-time detection and classification of people, cars, motorcycles, cats ✅ Courier identification + WhatsApp push notification (with AI summary + snapshot) ✅ Visual recognition of garage door and light status (full sun to nighttime) ✅ 8 cameras × 2 streams stable recording and retrieval ✅ Silent, passively cooled, stable under extreme heat ✅ Mobile video and snapshot review Screenshot of Frigate NVR bird's-eye view showing four outdoor cameras on a rainy winter day. Note the recent activity bar at the top with a bounding box around the person detected by the NPU, and the small car icon in the top-left of the garage camera indicating vehicle recognition. Additional Resources A detailed build guide (hardware BOM, software config, driver tuning, thermal validation) is available on Medium for anyone who wants to build their own system. The ArmSoM Sige7 delivers industrial-grade smart home NVR at 10W in 45°C Sydney heat – something x86 and Raspberry Pi cannot match.
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