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spatial-occupancy-event

Review video surveillance frames to detect spatial occupancy within specified regions of interest.

Updated Jun 2, 2026
Search Tags:#monitoring#ROI analysis#video analysis#video surveillance#spatial occupancy
One-click install
npx skills add https://github.com/bettercallfan/deerflow --skill spatial-occupancy-event
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: spatial-occupancy-event
Source: https://github.com/bettercallfan/deerflow/tree/main/skills/custom/video_surveillance/spatial-occupancy-event
Command: npx skills add https://github.com/bettercallfan/deerflow --skill spatial-occupancy-event

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill helps to determine whether visually observed subjects occupy configured video monitoring ROI regions through extracted-frame LLM review, useful for monitoring activities like illegal parking, lane occupancy, and intrusion.

Core Features & Use Cases

  • ROI Analysis: Reviews extracted frames to identify and confirm the presence of subjects within specified ROI regions.
  • Visual Confirmation: Provides visual evidence by analyzing frames and recording subject, ROI, timestamps, and evidence frame IDs.
  • Use Case: Detect if a vehicle is parked in a no-parking zone by analyzing video surveillance frames.

Quick Start

Analyze spatial occupancy for a video frame using the spatial-occupancy-event skill with --roi-matches-json <roi_matches.json> --tracks-json <tracks.json> --target-labels person --roi-types public_area --output <method_result.json>.

Frequently Asked Questions about spatial-occupancy-event

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect illegal parking or intrusion in video surveillance frames?▼

Spatial occupancy analysis detects subjects within configured regions of interest (ROI) by reviewing extracted video surveillance frames. It confirms the presence of targets like vehicles in no-parking zones and records evidence frame IDs.

What is spatial occupancy detection for ROI regions in video monitoring?▼

Spatial occupancy detection determines whether subjects occupy configured video monitoring ROI regions. It analyzes extracted frames to confirm subject presence within geometric boundaries and records timestamps with evidence frame IDs.

How do I analyze video frames for lane occupancy and public area surveillance?▼

Provide ROI matches and tracks JSON files with target labels and ROI types to analyze video frames. The process reviews extracted visual data to detect if specified subjects occupy defined geometric regions and outputs the method result.

Can I use video analysis to detect specific subjects in no-parking zones?▼

Yes, video analysis detects specific subjects in no-parking zones by reviewing extracted frames against specified target labels. It visually confirms occupancy and records the subject, ROI, timestamps, and evidence frame IDs for monitoring tasks.

What input data is required for ROI analysis in video surveillance?▼

ROI analysis requires ROI matches JSON, tracks JSON, target labels, ROI types, and an output path. These inputs enable the system to process extracted frames and identify subject occupancy within monitored geometric regions.

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