A vector database is a specialized system designed to store, index, and query high-dimensional numerical representations of data, known as vectors. Unlike traditional databases that rely on exact matches or keyword searches, vector databases use similarity metrics to find data points closest to a query vector. This is achieved through indexing techniques like hierarchical navigable small worlds (HNSW) or inverted file (IVF) systems, which organize vectors for efficient nearest-neighbor searches. For example, an image processed by a machine learning model might be represented as a 512-dimensional vector, capturing features like shapes, colors, or textures. A vector database allows developers to quickly retrieve images similar to a query by comparing these vectors, even across millions of entries.
In video surveillance, vector databases enable efficient analysis of large volumes of video data. Modern surveillance systems generate continuous streams of video, which can be processed frame-by-frame using computer vision models to extract vector embeddings. These embeddings represent objects (e.g., vehicles, people), actions (e.g., running, loitering), or facial features. For instance, a security team could search for all instances of a specific person wearing a red jacket across hours of footage. The system would convert a reference image of the jacket into a query vector and use the database to find matching vectors from stored video frames. This approach bypasses the need for manual review, which is time-consuming and error-prone with traditional methods.
The practical benefits of vector databases in surveillance include scalability and real-time processing. Video data is inherently high-volume, and vector databases are optimized to handle millions of vectors with low latency. For example, a parking lot surveillance system could use real-time object detection to flag unattended bags. Each detected object’s vector is stored and compared against known threat patterns, triggering alerts within seconds. Additionally, vector databases support temporal queries, such as tracking a vehicle’s movement across multiple cameras by linking vectors over time. This combination of efficient indexing, similarity search, and integration with machine learning pipelines makes vector databases a critical tool for modern video surveillance systems.