What are the best practices for video data preprocessing in search pipelines?

Video data preprocessing in search pipelines involves three key practices: efficient frame extraction, feature engineering, and standardized storage formats. The goal is to transform raw video into search-ready data while balancing quality and computational cost. Start by extracting frames at optimal intervals to capture meaningful content without redundancy. For example, using scene detection algorithms (like PySceneDetect) or fixed intervals (e.g., 1 frame per second) avoids processing near-identical frames. Resize frames to a consistent resolution (e.g., 224x224 for CNN-based models) and normalize pixel values to [0,1] or [-1,1] to ensure compatibility with machine learning models. Temporal downsampling (e.g., trimming silent sections in lecture videos) can further reduce processing load.

Feature extraction is critical for enabling semantic search. Use pre-trained models like ResNet for spatial features (objects, scenes) and 3D CNNs or transformer-based models (e.g., TimeSformer) for temporal patterns (actions, movements). For audio-heavy videos, extract MFCC coefficients or embeddings from models like VGGish. Dimensionality reduction (e.g., PCA) helps compress features without losing critical information. For example, reducing a 2048-dim ResNet embedding to 512 dimensions cuts storage costs while preserving 95% of variance. Always decouple preprocessing from indexing—store raw features and processed embeddings separately to allow reindexing with updated models.

Standardize storage and metadata to streamline retrieval. Use formats like HDF5 or TFRecords to store frames, audio, and features in chunked arrays for fast I/O. Include metadata such as timestamps, source video ID, and preprocessing parameters (e.g., frame rate used) in a structured format (JSON/Parquet). For large-scale systems, shard data by video duration or content type (e.g., sports vs. interviews) to enable parallel querying. Tools like FFmpeg for frame extraction and FAISS for vector indexing can be integrated into pipelines using workflow managers (Airflow, Kubeflow). For example, a pipeline might extract 1 fps frames, generate ResNet-50 embeddings, and store them in FAISS indices partitioned by video category, enabling low-latency similarity searches.