# Gemini Count In Video

Analyze and count objects in videos using Google Gemini API (object counting...

- Repository: https://github.com/benchflow-ai/skillsbench (1888 stars)
- Category: Media
- License: Apache-2.0
- Last updated: 2026-10-24
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add benchflow-ai/skillsbench --skill gemini-count-in-video -g -a claude-code -y`
Codex: `npx skills add benchflow-ai/skillsbench --skill gemini-count-in-video -g -a codex -y`
Cursor: `npx skills add benchflow-ai/skillsbench --skill gemini-count-in-video -g -a cursor -y`
Gemini CLI: `npx skills add benchflow-ai/skillsbench --skill gemini-count-in-video -g -a gemini-cli -y`

## Description

Analyze and count objects in videos using Google Gemini API (object counting, pedestrian detection, vehicle tracking, and surveillance video analysis).

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Gemini Video Understanding Skill

## Purpose
This skill enables video analysis and object counting using the Google Gemini API, with a focus on **counting pedestrians**, **detecting objects**, **tracking movement**, and **analyzing surveillance footage**. It supports precise prompting for differentiated counting (e.g., pedestrians vs cyclists vs vehicles).

## When to Use
- Counting pedestrians, vehicles, or other objects in surveillance videos
- Distinguishing between different types of objects (walkers vs cyclists, cars vs trucks)
- Analyzing traffic patterns and movement through a scene
- Processing multiple videos for batch object counting
- Extracting structured count data from video footage

## Required Libraries

The following Python libraries are required:

```python
from google import genai
from google.genai import types
import os
import time
```

## Input Requirements
- **File formats**: MP4, MPEG, MOV, AVI, FLV, MPG, WebM, WMV, 3GPP
- **Size constraints**:
  - Use inline bytes for small files (rule of thumb: <20MB).
  - Use the File API upload flow for larger videos (most surveillance footage).
  - Always wait for processing to complete before analysis.
- **Video quality**: Higher resolution provides better counting accuracy for distant objects
- **Duration**: Longer videos may require longer processing times; consider the full video length for accurate counting

## Output Schema
For object counting tasks, structure results as JSON:

```json
{
  "success": true,
  "video_file": "surveillance_001.mp4",
  "model": "gemini-2.0-flash-exp",
  "counts": {
    "pedestrians": 12,
    "cyclists": 3,
    "vehicles": 5
  },
  "notes": "Optional observations about the counting process or edge cases"
}
```

The rest: https://github.com/benchflow-ai/skillsbench/blob/HEAD/tasks-extra/pedestrian-traffic-counting/environment/skills/gemini-count-in-video/SKILL.md