Reducing computational load in video search involves optimizing how data is processed, stored, and queried. Three effective methods include leveraging keyframe extraction, using approximate nearest neighbor (ANN) search, and implementing metadata filtering. These approaches minimize redundant computations, streamline similarity searches, and narrow the search space efficiently.
First, keyframe extraction reduces processing by analyzing only representative frames instead of every video frame. Videos often contain redundant or similar frames, especially in static scenes. By identifying keyframes—such as those with scene changes or significant motion—developers can drastically cut the data volume. For example, a video at 30 FPS might be reduced from 1,800 frames per minute to 50–100 keyframes. Tools like FFmpeg or custom algorithms using histogram comparisons can automate this. Features like color histograms, edge detection, or CNN-based embeddings can then be extracted from these keyframes, reducing feature storage and comparison costs by orders of magnitude.
Second, approximate nearest neighbor (ANN) search accelerates similarity matching. Traditional exact search methods like brute-force comparison scale poorly with large datasets. ANN algorithms, such as FAISS, Annoy, or HNSW, trade a small accuracy loss for significant speed improvements. For instance, FAISS clusters vectors into buckets and searches within the most promising clusters, reducing comparisons from billions to millions. Developers can further optimize by quantizing high-dimensional embeddings into lower-bit representations (e.g., 8-bit integers instead of 32-bit floats), which cuts memory usage and speeds up distance calculations. These techniques are especially useful when searching for visually similar content across vast video libraries.
Third, metadata filtering narrows the search scope before applying compute-heavy algorithms. Metadata like timestamps, geolocation, object tags (from pre-processing with models like YOLO), or audio transcripts can act as filters. For example, searching for “a person waving in a park” could first filter videos tagged with “park” and “person,” then apply pose estimation only to those candidates. Similarly, optical character recognition (OCR) can pre-index text in videos, enabling fast keyword-based pre-filtering. This layered approach ensures that resource-intensive tasks like neural network inference run on a fraction of the original dataset, reducing overall computation. By combining these methods, developers balance accuracy and efficiency in video search systems.