Match detections or tracks against predefined camera ROI polygons.
npx skills add https://github.com/bettercallfan/deerflow --skill roi-mapping
Please help me install this Agent Skill. Skill: roi-mapping Source: https://github.com/bettercallfan/deerflow/tree/main/skills/custom/video_surveillance/roi-mapping Command: npx skills add https://github.com/bettercallfan/deerflow --skill roi-mapping
This Skill automatically matches detections or tracks against configured camera ROI polygons, providing the necessary spatial event detection before further processing.
Run the roi-mapping skill to match tracks against ROI polygons using the following command:
python roi-mapping/scripts/run.py --tracks-json <tracks.json> --camera-id <camera_id> --config <config.json> --output <roi_matches.json>High-intent search queries and answers about installing and using this skill.
You match tracks against camera ROI polygons by running a Python script that processes tracks JSON and config files, comparing spatial data like bounding boxes against the polygons and outputting ROI matches.
Spatial event detection identifies activities in specific camera regions like no-parking zones or sidewalks. You need ROI matching to filter detections or tracks so only relevant regions of interest are considered before further analysis.
Yes, you can use pre-existing tracks and bounding box data. The script accepts a tracks JSON file and a camera configuration file as input arguments to process and compare spatial data against predefined polygons.
No external dependencies are required for this video surveillance ROI matching script. It operates independently using a provided Python script to process spatial data and output ROI matches.
The best way to filter detections for no-parking zones or sidewalks is to match them against predefined camera ROI polygons, ensuring only relevant regions of interest are considered to enhance spatial event detection accuracy.
Using raw bounding box detections without ROI mapping risks processing irrelevant areas, which reduces accuracy. Matching against ROI polygons ensures spatial event detection focuses only on configured regions before further processing.
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