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name: detection-and-ai-components description: "Guides Viseron motion detection, object detection, post-processing, labels, masks, zones, and AI detector hardware or service choices." metadata: disco-role: operating disable-model-invocation: true license: MIT

Detection and AI Components

Use this sub-skill when the user is configuring or diagnosing Viseron's AI pipeline: motion detectors, object detectors, face recognition, image classification, license plate recognition, labels, filters, masks, zones, post-processing triggers, model paths, detector devices, or external AI services.

Route by task

• Choose a detector or backend: read detector selection before editing component blocks such as background_subtractor, mog2, mqtt, yolo, darknet, edgetpu, hailo, codeprojectai, deepstack, dlib, or compreface.
• Explain why detections do or do not trigger recordings: read labels, masks, zones, and filters, then check the candidate config with scripts/check_detection_config.py.
• Configure face recognition, image classification, license plate recognition, training folders, or object-triggered post-processors: read post-processing and training.
• Diagnose optional hardware, service, model, payload, motion-overlap, mask, or label failures: read troubleshooting.

Boundaries

This sub-skill owns detector and post-processor behavior only. Route camera URL acquisition, FFmpeg/GStreamer stream construction, NVR storage tiers, recordings, snapshots, timelapse, and live relay to camera-recording-pipeline. Route Docker installation, config-file basics, secrets, logging, webserver, and safe startup to configuration-and-deployment. Route MQTT entity publication, Home Assistant discovery, webhooks, notifications, PTZ commands, and template delivery to automation-and-integrations. Route writing new components/domains/tests or changing Viseron source to developer-extension.

Safe workflow defaults

1. Treat GPU, EdgeTPU, Hailo, external AI services, camera streams, containers, model downloads, and credentials as target-host requirements unless the user explicitly asks for live verification on that host.
2. Validate small detector snippets with the bundled checker before restarting Viseron; it parses YAML/JSON and never imports Viseron, OpenCV, ML frameworks, or service clients.
3. For recording-related questions, inspect the full path: object detector scanning gate, label/zone filters, trigger_event_recording, scan_on_motion_only, motion detector presence, and motion-overlap requirements.
4. For post-processors, first ensure the object detector tracks the source object labels; post-processors only receive objects that passed object-detector filtering.