Intelligent Security

In modern cities, security cameras are critical for monitoring traffic, detecting threats, and ensuring public safety. However, traditional surveillance systems rely heavily on centralized cloud processing, which introduces latency, increases bandwidth costs, and creates potential single points of failure. Every passing vehicle generates vast amounts of data—license plates, vehicle descriptions, and behavioral patterns—all of which need to be analyzed in real time to assess security risks. But with thousands of cameras constantly streaming footage to the cloud, congestion and slow response times become serious challenges.

VedgeDB transforms security infrastructure by enabling cameras to store and process data locally while still allowing global intelligence across the network. Instead of continuously streaming raw video to the cloud, each camera uses computer vision models to extract relevant features—such as vehicle make, model, color, and unusual movement patterns—and stores these embeddings in a local vector database. This allows the system to conduct similarity searches on-device, retrieving past occurrences of a vehicle or detecting anomalies without excessive cloud dependency.

When a security event occurs—such as a vehicle matching the description of a stolen car—VedgeDB routes the query through the cloud to retrieve the most relevant matches across the entire network. Instead of processing vast amounts of raw footage, the system searches against compact vector embeddings, allowing cameras to rapidly compare real-time observations with historical data from other locations. This hybrid approach ensures that security teams receive the most relevant insights with minimal latency, while still leveraging global data without requiring every node to store everything.

Network outages can cripple traditional cloud-based security systems, but with VedgeDB, critical data remains accessible at the edge. Even if a camera loses connection, it can still perform local searches and continue logging important events, ensuring continuous security coverage.

By decentralizing storage and computation, VedgeDB significantly reduces bandwidth costs while improving response times. Cameras no longer need to send raw video for processing; instead, they exchange only relevant query results and vector embeddings. This makes large-scale security networks more efficient, cost-effective, and responsive to real-time threats.

With VedgeDB, security systems become smarter, faster, and more scalable—bringing the power of real-time AI-driven surveillance to every connected camera.