# SOL Vision object registry Deployed October 10, 2026. Local viewer: or the workstation's current LAN address on port 8888. Public clients receive 403 for the viewer, frame input, JSON, and evidence routes. ## What is running `sol-object-registry.service` samples both existing camera managers every ten seconds. It never opens a V4L2 device or substitutes a camera. An atomic local frame route supplies the original upright image, capture ID, time, freshness, and screen-candidate geometry. Stale frames, warm-up placeholder images, and duplicate capture IDs cannot create sightings or missing-object events. The worker reuses the installed YOLO11n ONNX model through OpenVINO on CPU, with two inference threads and a 0.35 reporting threshold. Eighty ordinary object categories are available. Results are **provisional model labels**, not accepted identities. The spider retains its user-confirmed visual references, two recorded views, and rotation/scale reacquisition. Its name remains **unknown spider entity**, and only its face is overlaid. The registry adds no household alerts, person identities, or crossing counts. It does not draw generic detector guesses on the public camera feed. ## Records and meanings - SQLite authority: `/mnt/sol-data/sol-stack/object-registry/objects.sqlite3`. - Named objects have durable `obj-` identifiers. The imported spider is `obj-45c73f1de776426e`; its history begins at registry installation, not at the creation times of older reference images. - Generic detections have `track-` episode IDs. Association requires the same camera, category, detector, recent observation, and overlapping box, with one-to-one assignment. It is spatial continuity, not visual re-identification. After an episode is lost, a later generic detection gets a new episode. - `tentative`: one generic detection. `observed`: a named-reference match or repeated generic evidence. Neither confirms a model's semantic class. - `not_seen`: three fresh samples did not match; not proof of departure. - `unknown`: camera or detector observations unavailable; never counted as absence. The viewer also expires a stopped worker's current states. - Events include registration, first observation, repeated detection, reacquisition, unavailability, not-seen, and sampled sightings. Repeated sightings are written at most once per minute per object. - Evidence crops are local, mode 0600, and SHA-256 recorded. The generated evidence budget is 512 MiB; upon reaching it, metadata recording continues without new crops. Existing evidence is preserved. No public upload occurs. - `model_class_score` and `normalized_template_similarity` are separate measures; neither is a calibrated probability of identity. - Screen containment is recorded as context when available. The current screen polygon can be a fallback/incorrect candidate, so it does not establish that the object is physically in the room or on the television. ## Operation and enrollment ```bash systemctl --user status sol-object-registry.service python3 /home/david/random/bin/sol_object_registry.py status ``` Enroll another named object using a confirmed **upright** reference image and its pixel crop (x, y, width, height): ```bash python3 /home/david/random/bin/sol_object_registry.py enroll \ --camera record-player --label 'chosen name' \ --image /path/to/upright-reference.png --bbox 100 100 40 50 ``` `--camera c615` is also supported. Reference matching is camera-scoped. New named entries and the initial spider import are persisted in SQLite; a generated `~/.local/state/sol-vision-camera/named-objects.json` projection keeps the existing renderer and worker in agreement. Do not independently edit that projection as the registry authority. Raw/secondary overlays are unchanged; secondary named observations appear in the registry. Stopping the registry service leaves camera ownership and playback alone. It refuses to start if `/mnt/sol-data` is not mounted. The loopback worker API is port 18919; the existing dashboard proxies only allowlisted read routes. The viewer is read-only; enrollment is a local CLI operation. ## Installed inventory and online comparison The search covered user services, container inventory, Flatpak applications, system packages and command-line tools, Python packages, installed model files, and the prior detector benchmark reports. Installed and relevant: | Utility | Role and decision | |---|---| | OpenCV 4.13 system / 5.0 detector venv | Existing capture/tracking, reference matching, image processing. Reused. | | OpenVINO 2026.3.1 + YOLO11n ONNX | Bounded CPU generic detector. Reused in the worker. | | Ultralytics 8.4.150 + YOLOE-11s + MobileCLIP | Installed text/visual prompting option. Benchmarked; not enabled for alerts or substituted for user identities. | | PyTorch 2.14 CPU / torchvision 0.29 | Installed detector environment; no CUDA inference assumed. | | MediaPipe ONNX/HOG | Existing person-candidate system; untouched. | | SmolVLM 500M | Existing scene-description path; prose remains hypothesis rather than database identity. | | Motion 4.3.2 | Installed isolated diagnostic, disabled after noisy earlier trials. Not activated. | | Tesseract, FFmpeg, GStreamer | Installed OCR/transport tools; not object registries. | | Frigate | No running container found; earlier download was paused. Not installed during this change. | Official options reviewed: - [YOLOE](https://docs.ultralytics.com/models/yoloe): text/visual-prompt detection and segmentation. It supports custom categories, but the QuickCam still needs scene-specific validation. Visual-prompt class IDs require application naming. - [Ultralytics tracking](https://docs.ultralytics.com/modes/track): multiple tracking backends, including ByteTrack and BoT-SORT. Useful for a higher-rate continuous tracker, but their IDs alone are not durable real-world identities. - [Frigate](https://docs.frigate.video/configuration/review/): full NVR with review timelines and tracked objects. Broader than the current registry need; replacing the existing capture/display stack is unnecessary here. - [SAM 2](https://github.com/facebookresearch/sam2) and [SAM 3](https://github.com/facebookresearch/sam3/blob/main/README.md): richer segmentation/tracking options. No additional weights were installed; current face-only labeling does not require mask inference. - [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO): open-set text-guided detection, a future alternative requiring local evaluation. - [Norfair](https://tryolabs.github.io/norfair/dev/reference/): configurable online detection association. A tracking library, not an object database. The registry is detector-independent at its ingestion boundary; additional detectors can submit source-qualified observations without replacing the catalogue or conflating their scores. ## Validation and limits Initial replay: both installed generic models reported no >=0.35 detections on the current QuickCam reference. The first C615 download was a warm-up placeholder and is **excluded** as a valid camera benchmark. Subsequent fresh C615 live observations included a bottle candidate; a saved crop shows the bottle. The QuickCam also produced intermittent chair candidates. These are logged as provisional, not treated as proof of reliable general recognition. The low-resolution camera, changes of viewpoint, low light, occlusion, and reference aging still limit detection. Enrollment helps a known object; it does not give the classifier universal recognition. Face templates are not human facial recognition. 41 unit checks passed across the registry and existing named-object, vision, music, tracking and dashboard suites. Lifecycle checks cover durable IDs, duplicate samples, stale/offline sources, detector failure, multiple same-class objects, camera separation, import idempotency, and reacquisition. Live checks cover both sources, private evidence, viewer rendering, public rejection, and restart persistence. Raw research artifacts are private under `/mnt/sol-data/sol-stack/object-registry/research/`.