Identification of new commercials using repeated video sequence detection

Abstract

Automated commercial detection can be performed by matching features extracted from commercials or by detecting embedded codes that are hidden within the commercial. In both cases, it is necessary to create a database of known commercials that contain the information necessary for detection. In this paper, we present an automated technique for locating previously unknown commercials by continuously monitoring broadcast television signals. Our system has two components: repeated video sequence detection, and feature-based classification of video sequences as commercials or non-commercials. Our system utilizes customized temporal video segmentation techniques to automatically partition the digital video signal into semantically sensible shots and scenes. As each frame of the video source is processed, we extract auxiliary information to facilitate repeated sequence detection. When the video transition marking the end of the shot/scene is detected, we are able to rapidly locate all previous occurrences of the video clip. In order to classify video sequences as commercials or non-commercials, we extract a number of features from each video sequence that characterize the temporal and chromatic variations within the clip. We have evaluated three classification approaches using this information and have consistently achieved over 90% accuracy identifying new commercials and non-commercials as they are broadcast.