VideoDB Documentation
Partnerships
VideoDB x TwelveLabs: Real-Time Video Understanding
Human Monitoring is Expensive, Exhausting, and Doesn't Scale
From baby monitors to warehouse cameras or endless CCTV feeds, many companies still depend on human eyes to monitor live video—a costly, tedious, and highly error-prone approach. Fatigue inevitably sets in, accuracy declines, and scaling up means hiring more personnel rather than enhancing systems.
But in an AI-driven era, monitoring shouldn’t be manual, miss critical details, or become a bottleneck. Imagine receiving instant alerts when packages are stolen, notifying parents the moment a baby attempts to climb out of a crib, proactively highlighting safety risks before they escalate, or doctors instantly knowing when ICU patients need immediate attention.
This is precisely the responsive monitoring delivers through its real-time infrastructure. VideoDB now provide first party integration with ’ advanced Pegasus 1.2 AI model for precise frame understanding.
Live video to Instant Action
Real-time video analysis isn’t merely a nice-to-have—it's a fundamental shift in capability.
The opportunities are immense, yet technical barriers have historically prevented many development teams from unlocking this potential.
Reality of Building Video AI
If you've ever attempted to build a robust video understanding system, you know the pain firsthand. You're often stuck playing the role of a systems integrator, wrestling with:
These challenges can quickly stall innovation, turning promising ideas into lengthy, frustrating engineering endeavors.
Simplifying Video AI Infrastructure with VideoDB
VideoDB is a purpose-built infrastructure for AI driven video management. It provides a unified, AI-native infrastructure handling the complete lifecycle of video content—from ingestion and indexing to alert management—all via a streamlined, developer-friendly API.
Leveraging VideoDB, you can effortlessly ingest multiple real-time video streams, manage customizable indexes uniquely tailored for specific analyses, and simultaneously analyze diverse aspects of a single video stream. Additionally, VideoDB’s targeted alerting system triggers automated webhooks, ensuring rapid responses to critical events.
And now, we've equipped that powerful infrastructure with an even more powerful intelligence.
Introducing the TwelveLabs Integration: Your Frame Understanding, Supercharged
We're excited to announce our native integration with TwelveLabs—going far beyond a traditional partnership. We've seamlessly embedded TwelveLabs' advanced AI, powered by the exceptional Pegasus 1.2 model, directly within VideoDB.
What does that mean for you?
Now, accessing TwelveLabs' sophisticated video understanding models, like Pegasus, is as easy as adding a single parameter to your indexing call. All the AI power you need is fully integrated into your VideoDB environment—effortlessly blending world-class intelligence with unmatched simplicity.
Real-World Use Cases: From Theory to Action
With VideoDB + TwelveLabs, sophisticated real-time video monitoring is just minutes away:
Imagine a camera monitoring a dry riverbed in a flood-prone area. With TwelveLabs integrated into VideoDB, you can continuously detect critical events—like rapidly rising floodwaters—in real-time. The moment Pegasus recognizes the signs of a flash flood, VideoDB immediately triggers life-saving alerts. This isn't just video analysis; it's proactive, intelligent disaster prevention.
After a long day, parents deserve restful, worry-free sleep. With VideoDB’s real-time monitoring powered by TwelveLabs' Pegasus, you'll instantly know if your baby tries climbing out of the crib or needs immediate attention. Sleep easy, knowing VideoDB and TwelveLabs have you covered—every second of the night.
How It Works: One Line of Code to Unlock a New World
Ready to see how simple this is? To index a video stream with TwelveLabs' powerful Pegasus model, you just specify it as the model_name.
from videodb import SceneExtractionType
# Your existing stream object
flood_stream = <...>
# Index scenes using TwelveLabs' model
flood_scene_index = flood_stream.index_scenes(
extraction_type=SceneExtractionType.time_based,
extraction_config={
"time": 10,
"frame_count": 6,
},
prompt="Monitor the dry riverbed and surrounding area. If moving water is detected across the land, identify it as a flash flood and describe the scene.",
# This is the magic line!
model_name="twelvelabs-pegasus-1.2",
name="Flash_Flood_Detection_Index"
)
print("Scene Index ID:", flood_scene_index.rtstream_index_id)
This integration lets you seamlessly test out TwelveLabs models right inside VideoDB's indexing pipeline—giving you maximum flexibility, control, and ease. Now, you can instantly build real-time visual understanding apps without friction. We can't wait to see what you create!
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