Tv content segmentation, categorization and identification and time-aligned applications

Abstract

Content segmentation, categorization and identification methods are described. Content tracking approaches are illustrated that are suitable for large scale deployment. Time-aligned applications such as multi-language selection, customized advertisements, second screen services and content monitoring applications can be economically deployed at large scales. A client performs fingerprinting, scene change detection, audio turn detection, and logo detection on incoming video and gathers database search results, logos and text to identify and segment video streams into content, promos, and commercials. A learning engine is configured to learn rules for optimal identification and segmentation at each client for each channel and program. Content sensed at the client site is tracked with reduced computation and applications are executed with timing precision. A user interface for time-aligned publishing of content and subsequent usage and interaction on one or more displays is also described.