Large-scale, real-time logo recognition in broadcast videos

Robust, real-time, multi-class logo detection in high resolution broadcast videos presents several difficult challenges. For most logos we only have a few training samples, which makes training robust classifiers hard. Also, logos could potentially occur anywhere in the image, and traditional sliding window approaches for logo/object detection are computationally intensive. We present a system that addresses these issues by first identifying a small set of possible logo locations in a frame, based on temporal continuity and multi-resolution search, and then successively pruning these locations for each logo template, using a cascade of color and edge based features. We present experimental results that demonstrate our system for detecting a total of 270 different logo classes in broadcast video from 5 different languages (English, Indonesian, Malay, Simplified and Traditional Chinese).