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Storytime: Why This Matters

Meet Vidit and Meghna — a young couple juggling demanding jobs and household responsibilities. After a long, exhausting day, all they hope for is a peaceful night’s sleep. But their energetic little one has other plans. Their child, once safely tucked into his crib, has recently discovered how to climb out. While the parents sleep, unaware, the baby risks injury by wandering unsupervised at night. How can they keep him safe without losing their much-needed rest?

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Enter VideoDB RTStream

VideoDB offers the perfect solution for this problem. Using RTStream, we can let AI continuously monitor a live video feed, index scenes, detect specific events like baby attempting to climb out of the crib, and instantly send alerts to the parents when something risky happens. In this guide, Vidit and Meghna install an IP camera near the crib and use VideoDB RTStream to power an AI monitoring system. As soon as the baby makes a move to climb out, AI detects it, triggers an event, and fires a real-time alert so the parents can step in.

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What You’ll Learn

By the end of this guide, you’ll learn how to:
  • Connect a live RTSP video stream to VideoDB
  • Continuously analyze video scenes using AI-generated natural language descriptions
  • Detect specific events like “baby escaping crib”
  • Trigger real-time alerts on such events

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Setup

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Install Dependencies

pip install videodb

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Connect to VideoDB

import videodb

api_key = "your_api_key"
conn = videodb.connect(api_key=api_key)
coll = conn.get_collection()

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Implementation

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Step 1: Connect to the RTSP Stream

Connect to the live video stream of the crib using its RTSP URL. In this demo, the stream is running at rtsp://samples.rts.videodb.io:8554/crib. 
rtsp_url = "rtsp://samples.rts.videodb.io:8554/crib"
crib_stream = coll.connect_rtstream(
    name="Baby Crib Monitor",
    url=rtsp_url,
)

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Step 2: Index Scenes with AI Descriptions

Create a real-time scene index that periodically analyzes the video and generates natural language descriptions of what’s happening in the crib. The AI model watches for activity such as the baby moving, sitting, or attempting to climb out. 
crib_scene_index = crib_stream.index_visuals(
    batch_config={
        "type": "time",
        "value": 10,
        "frame_count": 1,
    },
    prompt="Describe the activity of the baby kept inside a baby crib. Notice if baby climbs out or attempts to escape.",
    name="Baby_Crib_Index",
)
The batch_config defines how frequently the AI analyzes the stream:
  • value: 10 - Analyze every 10 seconds
  • frame_count: 1 - Extract 1 frame per analysis window

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Step 3: Define an Event for Baby Escape

Create an event in VideoDB to detect when the AI spots the baby attempting to climb out. 
event_id = conn.create_event(
    event_prompt="Detect if the baby is trying to escape or climbing out of the crib.",
    label="baby_escape",
)
The event acts as a filter - when the AI’s scene description matches this prompt, it triggers the event.

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Step 4: Attach an Alert for Real-Time Notifications

Link a real-time alert to this event, which will notify the parents instantly through a webhook. 
webhook_url = "https://your-webhook-url.com"

alert_id = crib_scene_index.create_alert(
    event_id,
    callback_url=webhook_url,
)

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What You Receive

When a baby escape attempt is detected, your webhook receives a detailed alert payload: 
{
  "event_id": "event-3adc40d26d6fed0d",
  "label": "baby_escape",
  "confidence": 0.95,
  "explanation": "The baby is actively trying to climb out of the crib by holding onto the top rail and attempting to pull itself up, which indicates an escape attempt.",
  "timestamp": "2025-05-28T23:36:39.979133+00:00",
  "start_time": "2025-05-29T05:06:36.612197+05:30",
  "end_time": "2025-05-29T05:06:46.612197+05:30",
  "stream_url": "https://rt.stream.videodb.io/manifests/rts-019711a0-0fde-7911-b282-25bc0b4ecf65/1748475396000000-1748475407000000.m3u8"
}

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Wrapping Up: Peace of Mind

With this system in place, Vidit and Meghna can finally sleep peacefully, knowing their child is being safely monitored through AI-driven surveillance. But this is just one story. What if the same system could:
  • Monitor an elderly parent at home — detecting falls or prolonged inactivity?
  • Watch over a pet while the family is away, alerting them if it leaves a safe zone?
  • Notify parents when a toddler approaches dangerous areas like staircases or kitchen counters?
The possibilities of real-time video intelligence at home are endless. What would you monitor next?

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