Midv-550 Work

MIDV-550 — Overview and Analysis

7. Deployment & Installation Guide (High‑Level)

1. Rack Mount & Power

   • Install the 4U chassis in a standard 19‑inch rack.
   • Connect both redundant 120 V/240 V AC power supplies.

2. Insert I/O Modules

   • Slide the SDI carrier board into slot 1, HDMI into slot 2, etc.
   • Secure with the locking lever; the system automatically detects and configures each module.

3. Network & Storage

   • Connect 10 GbE uplink to the production network (or SRT‑compatible router).
   • Insert NVMe SSDs for recording or model storage; the OS will RAID‑1 them by default.

4. Initial Software Setup

   • Power on; the LCD shows “Booting”.
   • Access the web UI via the default IP 192.168.100.10 (DHCP or static).
   • Change admin password, configure NTP, and apply license keys (if any).

5. AI Model Deployment

   • In the AI Manager tab, upload an ONNX model (model.onnx).
   • Define the processing pipeline (e.g., SDI‑1 → Pre‑proc → NPU → Encode → RTSP).
   • Test with the live preview; adjust confidence thresholds.

6. Live Operation

   • Use GStreamer or the SDK to route video:

     gst-launch-1.0 midvsrc device=sdI0 ! video/x-raw,format=NV12,width=7680,height=4320,framerate=60/1 ! \
     midvai model=/opt/models/yolo.onnx ! \
     x264enc bitrate=50000 ! rtspclientsink location=rtsp://192.168.1.100:8554/stream
     

   • Monitor latency and CPU/NPU load via the dashboard.

7. Maintenance

   • Firmware upgrades are applied through the web UI (zero‑downtime).
   • Hot‑swap any failed I/O carrier while the system continues processing other streams.

---

Title Report: MIDV-550

Common research directions using MIDV-550

• Robust mobile capture pipelines that handle glare, folds, and occlusions.
• End-to-end approaches combining detection, rectification, and OCR in a single trainable model.
• Domain adaptation from studio-scanned documents to in-the-wild smartphone photos.
• Automated forgery and tamper detection applied to real-world ID captures.
• Lightweight models for on-device processing with constrained resources.

2. Cast & Crew

• Actress: Aika Yamagishi (山岸逢花)
• Director: Kyousei (キョウセイ)
• Series: (Standalone release)

4.1. Video Processing Pipeline

| Stage | Function | Typical Latency | |-------|----------|-----------------| | Capture | Multi‑format (SDI/HDMI) digitization, de‑interlacing, colour‑space conversion (RGB↔YUV) | 0.5 ms | | Pre‑Processing | Scaling, cropping, HDR tone‑mapping, noise reduction | 1–2 ms | | AI Inference | On‑board NPU runs models (e.g., YOLO‑v5, OpenPose) on each frame | 2–4 ms | | Encoding | H.264, H.265, AV1, JPEG‑XS (hardware accelerators) | 2 ms (8K@60) | | Transport | SDI output, RTP/RTSP over 10 GbE, NDI, SRT | <0.5 ms | | Total End‑to‑End | ≈ 6–9 ms (depending on resolution & AI load) | |

Typical tasks evaluated with MIDV-550

• Document detection and localization: Detecting that a document exists in an image and estimating its bounding polygon or corners. Performance is measured via intersection-over-union (IoU), corner localization error, or homography reprojection error.
• Document rectification (dewarping): Estimating a homography to transform the captured quadrilateral into a frontal, axis-aligned image suitable for OCR. Quality can be measured by reprojection error or downstream OCR accuracy.
• Segmentation and layout analysis: Identifying regions such as photo, MRZ (machine-readable zone), name, date of birth, and other fields. Metrics include pixel-wise IoU and region detection F1.
• OCR and field extraction: Recognizing text in the whole document or extracting structured fields (name, document number, expiry). Evaluated via character error rate (CER), word error rate (WER), and field-level accuracy.
• Forgery and tampering detection: Though MIDV-550 primarily targets recognition tasks, its realistic imaging conditions are also useful for research into detecting alterations, printed overlays, or subtle tampering.