How are embeddings used in video analytics?

Embeddings in video analytics are numerical representations of video content that capture semantic features like objects, actions, or scenes. These vectors, generated by machine learning models, enable efficient comparison and analysis of video data. By converting frames or sequences into compact, high-dimensional vectors, embeddings abstract raw pixel data into meaningful patterns, making it easier to perform tasks like search, classification, or anomaly detection at scale.

A common use case is object tracking across video frames. For example, a model might generate embeddings for detected objects (e.g., a person or vehicle) in each frame. By comparing these embeddings over time, the system can track the same object even if its appearance changes due to lighting, angle, or occlusion. Similarly, in video retrieval systems, embeddings allow users to search for specific scenes—like "a red car turning left"—by comparing query embeddings to precomputed video segment embeddings. Platforms like security systems or content archives use this to locate relevant footage without manual tagging. Another example is anomaly detection: embeddings from normal operations (e.g., factory assembly lines) can be compared to real-time video embeddings to flag deviations, such as unexpected object movements.

Technically, embeddings are often extracted using convolutional neural networks (CNNs) like ResNet for frame-level features or 3D CNNs for spatiotemporal sequences. For temporal context, models like Transformers or I3D (Inflated 3D ConvNet) process video clips to capture motion patterns. Developers typically fine-tune pre-trained models on domain-specific data—for instance, training a model on traffic camera footage to improve vehicle embedding accuracy. Embeddings are stored in vector databases (e.g., FAISS, Milvus) optimized for fast similarity searches. When deploying, engineers balance embedding dimensionality (e.g., 512-1024 dimensions) to preserve information while minimizing computational overhead. For real-time applications, optimizations like frame sampling or model quantization help maintain performance without sacrificing critical details.