Static Logo Detection in Video Frames Using Multi-Feature Confidence

The detection method is segmented into three independent modules: pixel grayscale difference calculation, edge gradient calculation, and local direction correlation calculation. Each module processes a specific aspect of logo detection and produces separate confidence scores that are later integrated. This segmentation allows each module to be optimized independently while maintaining overall detection accuracy.

The patent combines multiple detection features (grayscale difference, edge gradient, direction correlation) into a unified detection framework. Each feature contributes a confidence score, and these scores are merged through weighted integration to produce a final detection result. This merging approach leverages the strengths of each individual feature to achieve high detection accuracy.

The patent applies local quality by calculating detection features in local neighborhoods rather than globally. The local direction correlation is computed within a predefined neighborhood around each pixel, and confidence scores are aggregated locally before final integration. This local approach reduces computational complexity compared to global calculations while maintaining detection precision.

The method computes only the essential detection features needed for accurate logo detection rather than analyzing all possible image properties. By selecting and computing only the most relevant features (grayscale difference, edge gradient, direction correlation), the system achieves high precision without unnecessary computational overhead.