Video embeddings

Video embeddings are fixed-length, high-dimensional numerical vectors that distill a video's full spatio-temporal content for machine-efficient processing.

Video embeddings transform raw footage—millions of pixels across space and time—into a compact, searchable vector space. This process relies on specialized deep learning models: 3D Convolutional Neural Networks (3D CNNs) like I3D or transformer-based architectures like ViViT extract both spatial (visual) and temporal (motion) features from sampled frames. The resulting vector, often 512 to 1024 dimensions, represents the video's semantic meaning (e.g., 'a dog playing fetch') rather than raw data. Stored in a vector database, these embeddings power critical AI applications: sub-second content-based video retrieval, real-time anomaly detection in surveillance feeds, and high-precision content moderation systems.

https://zilliz.com/learn/video-embeddings-ai-visual-data-representation