Vector search methods for video surveillance need to balance speed, accuracy, and scalability to handle large volumes of high-dimensional data like video frames or object embeddings. Three key approaches include approximate nearest neighbor (ANN) algorithms, hierarchical navigable small world (HNSW) graphs, and inverted file (IVF) indexing. These methods enable efficient similarity searches across frames or objects, such as identifying a person or vehicle across multiple camera feeds. Each technique has trade-offs between query speed, memory usage, and precision, making them suitable for different surveillance scenarios.
ANN algorithms like FAISS (Facebook AI Similarity Search) or Annoy (Approximate Nearest Neighbors Oh Yeah) are widely used for real-time video analysis. For example, FAISS optimizes GPU acceleration to search billions of vectors quickly, which is critical for processing live surveillance streams. HNSW graphs excel in scenarios requiring high recall with low latency, such as re-identifying a suspect across non-overlapping camera views. IVF indexing, which clusters vectors into groups, works well for batch processing archived footage, where slightly slower queries are acceptable. Developers can combine these methods; for instance, using IVF to pre-filter data and HNSW for refined searches.
Practical implementation considerations include embedding extraction and hardware constraints. For video, embeddings are often generated using CNNs (e.g., ResNet for objects) or transformers (e.g., ViT for scenes). Storing these embeddings in a vector database like Milvus or Elasticsearch allows integration with ANN or HNSW for scalable searches. Edge devices might use lightweight methods like PQ (Product Quantization) to compress vectors, reducing memory usage while maintaining search accuracy. For example, a parking lot surveillance system could use PQ-compressed embeddings to quickly locate vehicles matching a specific color or model across terabytes of footage. Developers should test combinations of these methods to align with specific latency, storage, and accuracy requirements.