# AI Baby Monitor: A Local Video-LLM Powered Solution for Child Safety

This repository profile is provided by osrepos.com, an open source repository discovery platform.

Source: osrepos.com
Repository profile: https://osrepos.com/repo/zeenolife-ai-baby-monitor
Generated for open source discovery and AI-assisted research.

The AI Baby Monitor is an innovative, privacy-first solution that leverages local Video-LLMs to enhance child supervision. It monitors a video stream against user-defined safety rules, issuing a gentle beep if a rule is broken. This tool acts as an additional pair of eyes, providing real-time alerts without compromising privacy.

GitHub: https://github.com/zeenolife/ai-baby-monitor
OSRepos URL: https://osrepos.com/repo/zeenolife-ai-baby-monitor

## Summary

The AI Baby Monitor is an innovative, privacy-first solution that leverages local Video-LLMs to enhance child supervision. It monitors a video stream against user-defined safety rules, issuing a gentle beep if a rule is broken. This tool acts as an additional pair of eyes, providing real-time alerts without compromising privacy.

## Topics

- baby-monitor
- video-llm
- Python
- AI
- LLM
- privacy
- home-security
- open-source

## Repository Information

Last analyzed by OSRepos: Sun Feb 22 2026 21:33:56 GMT+0000 (Western European Standard Time)
Detail views: 2
GitHub clicks: 0

## Safety Notice

OSRepos shares public repositories for knowledge and discovery only. Review source code, dependencies, licenses, and security implications before running or installing anything.

## Content

## Introduction

The AI Baby Monitor is a powerful, privacy-first project that utilizes local Video-LLMs to provide an intelligent monitoring system for children. Designed to be your "second pair of eyes," it watches a video stream from a webcam or RTSP camera and applies a simple list of user-defined safety rules. If a rule is broken, the system issues a single, gentle beep, prompting you to quickly check on your baby. Everything runs locally on your network, ensuring complete privacy.

## Installation

Getting started with the AI Baby Monitor requires Docker, docker-compose, a GPU, and Python 3.12 with `uv`.

**Prerequisites**: Docker + docker-compose, One GPU, Python 3.12 with [uv](https://github.com/astral-sh/uv){:target="_blank"}

bash
# 1 — clone the repository
$ git clone https://github.com/zeenolife/ai-baby-monitor.git && cd ai-baby-monitor

# 2 — copy .env.template into .env
$ cp .env.template .env

# 3 — build & start all services (Redis, vLLM, video streamer, Streamlit viewer)
$ docker compose up --build -d

# 4 — start the watcher on the host. This is necessary for sound playback.
$ uv run scripts/run_watcher.py --config-file configs/living_room.yaml

# 5 — open the dashboard ? http://localhost:8501. You can also open the dashboard on your phone http://{host_network_ip}:8501


Please note, the first run will download the model (approximately 6 GB), build the Docker image, and may take a few minutes.

## Examples

The project includes compelling demos that illustrate its functionality without putting children in danger.

*   **"No smartphones" rule**: A demo shows an alert being fired when people are using smartphones, violating a predefined rule.
*   **Baby walking safely**: Another demo illustrates the system correctly identifying a baby walking safely, with no alert issued.

These examples highlight the system's ability to discern between safe and potentially unsafe situations based on your custom rules.

## Why Use It

The AI Baby Monitor stands out for several key reasons:

*   **Private-first**: All processing occurs locally, ensuring no data ever leaves your network. This commitment to privacy is paramount for sensitive monitoring applications.
*   **Realtime-ish Performance**: It operates efficiently on consumer GPUs, processing at approximately one request per second, making it practical for home use.
*   **Advanced Video LLM**: By default, it uses the Qwen2.5 VL model, served through vLLM, providing sophisticated video analysis capabilities.
*   **Minimalist Alerts**: A single, gentle beep serves as a deliberate, quiet notification, designed to prompt a quick glance rather than cause alarm.
*   **Live Dashboard**: A Streamlit viewer offers a live stream and real-time LLM reasoning logs, giving you full transparency into the system's operations.
*   **Easy Configuration**: Safety rules are defined in natural language within simple YAML files, making customization straightforward.
*   **Multi-room Support**: The system can be configured to monitor multiple rooms simultaneously, adapting to various home setups.

It is important to remember that this project is not a replacement for adult supervision. It is intended as an additional safeguard for those brief moments of distraction, providing a timely alert when needed.

## Links

*   **GitHub Repository**: [https://github.com/zeenolife/ai-baby-monitor](https://github.com/zeenolife/ai-baby-monitor){:target="_blank"}
*   **DeepWiki**: [https://deepwiki.com/zeenolife/ai-baby-monitor](https://deepwiki.com/zeenolife/ai-baby-monitor){:target="_blank"}