/physical-ai-video-data-augmentation

Piazza VLM prompt for event extraction.

Runtime mount target: /workspace/configs/video_event_analysis_prompt_redid.md.

Parsing gate for this scene:

1. Output exactly two JSON objects.
2. JSON object #1 is clip metadata only.
3. JSON object #2 contains event annotations under "events".
4. No markdown wrappers, comments, or trailing explanation.

Scene profile:

• Outdoor cobblestone piazza viewed from above.
• Pedestrian flow, cafe seating, and scooter/motorcycle activity coexist in frame.

Analyst task: Detect pedestrian-safety and two-wheeler risk events from each segment.

Recognized event concepts:

• motorcycle_pedestrian_contact
• pedestrian_collision
• near_miss_motorcycle
• pedestrian_close_call
• erratic_motorcycle
• pathway_obstruction
• pedestrian_flow
• outdoor_dining
• motorcycle_parking

Do not invent categories not listed above.

JSON #1 (metadata) must include:

• version, video_id, format, rectified, scenario_info, scene_description, event_summary, fps, duration, height, width, camera_id
• scenario_info fixed to "OUTDOOR_PIAZZA"
• scene_description should cover square geometry, cafe footprint, parked two-wheelers, weather, lighting, and unusual route blockages
• event_summary should capture movement intensity and timestamped safety outcomes

JSON #2 (event annotations) must include:

• top-level: version, events
• per-event keys: event_id, start_time, end_time, category, sub_category, instances, event_caption
• allowed category values: collision, near_miss, anomaly, normal_traffic

Category mapping:

• collision => motorcycle_pedestrian_contact, pedestrian_collision
• near_miss => near_miss_motorcycle, pedestrian_close_call
• anomaly => erratic_motorcycle, pathway_obstruction
• normal_traffic => pedestrian_flow, outdoor_dining, motorcycle_parking

Field-level constraints:

• sub_category is always a JSON list (example: ["near_miss_motorcycle"])
• event_caption includes severity (low/medium/high), actors, and evidence window
• Use track IDs when present; otherwise use human-readable actor labels
• Timestamps are numeric seconds

No-activity case:

• If the piazza is empty, return metadata and set events to an empty list.

Piazza-specific interpretation notes:

• Canopy cover causes temporary occlusion; disappearance under awnings is not itself anomalous.
• Parked scooters are baseline context; only moving vehicles should drive near_miss or collision calls.
• Tables/chairs are fixed infrastructure unless clearly displaced into travel paths.
• Strong shadow and reflection artifacts on stone may resemble entities; confirm with motion cues.
• Top-down perspective compresses distance; require visible evasive behavior before labeling a near miss.
• In rain, reflected silhouettes can duplicate apparent objects; prioritize track continuity over glare.

Return order is strict: JSON #1 then JSON #2.