Individual skill
evo-pedestrian-vlm-counting
Count unique pedestrians in surveillance video frames using Vision LLM APIs (OpenAI GPT-4o or Google Gemini) and write results to Excel. Use when you need to analyze surveillance footage for pedestrian counting, perform person re-identification across video frames, or generate Excel reports of pedestrian counts per video file. Supports both OpenAI and Gemini vision APIs with robust response parsing.
tasks-evolved/pedestrian-traffic-counting/environment/skills/evo-pedestrian-vlm-counting/SKILL.md
Source commit 84ca182da055fafb6453dcec4a6defdbbfd987c1
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- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
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- No
- Observed behavior
- Local scripts
- Last checked
- Nov 6, 2026
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Repository context
- Repository stars (not skill ratings)
- 97
- Repository last updated
- Sep 10, 2026
- License
- Apache-2.0
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- agent-skills Repository topic
- ai-agents Repository topic
- llm-agents Repository topic
- open-world Repository topic
- self-evolving-agents Repository topic
- engineering Derived category
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SKILL.mdcore · 3,677 bytesSKILL.mdcore · 3,677 bytesscripts/pedestrian_counting.pyruns · 10,243 bytes
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# Pedestrian VLM Counting
Sends extracted video frames to Vision LLM APIs with carefully engineered prompts for unique pedestrian counting, parses integer results robustly, and writes final counts to a properly formatted Excel file.
## When to Use
- Counting unique pedestrians in surveillance video footage
- Analyzing foot traffic across multiple video files
- Generating Excel reports of pedestrian counts per video
- Using vision AI to perform person re-identification across frames
## Dependencies
This skill depends on `evo-video-frame-extraction` for frame extraction. Import frame extraction functions from that skill.