Highlight Learning Module for Sports Video Event Detection

The patent segments the video processing task into multiple components: low-level feature extraction (color, motion, texture), mid-level event detection (throws, catches, runs), and high-level highlight classification. This segmentation allows each component to be optimized independently and processed in parallel, dramatically reducing the time to identify highlights compared to traditional frame-by-frame analysis.

The system performs preliminary action by pre-training event models and classifiers on labeled training data before actual highlight detection. The event models are trained to recognize recurring events in sports videos, and classifiers are trained to distinguish highlights from non-highlights based on event vectors. This preliminary training enables rapid highlight identification during actual video processing without requiring viewers to watch entire games.

The system implements self-service through automated event detection and highlight classification. The event models automatically detect recurring events in videos by analyzing low-level features, and the trained classifiers automatically determine which segments are highlights. This eliminates the need for manual annotation while maintaining high accuracy, as the system learns from training data and applies the learned patterns automatically to new videos.

The patent replaces manual mechanical analysis with automated computational processes. Instead of human viewers watching and identifying highlights manually, the system uses computer vision algorithms to extract features, event models to detect patterns, and machine learning classifiers to identify highlights. This substitution of mechanical human analysis with automated computational systems maintains accuracy while reducing operational complexity.