Abstract
Television news channels broadcast different kinds of content like debates, interviews, commercials along with news presentations. Real-time detection of these news programs or their retrieval from large volumes of stored broadcast videos is a challenging problem and is a necessary first step for broadcast analytics. News program detection is even harder in Indian context where closed caption text or program markers are not provided by TV news channels (not mandated by law). We propose a two-stage approach to classify news video segments. First, broadcast video shots are classified with multiple labels based on a set of audio-visual features. Second, sequences of these shot features are modeled to detect news programs. Another contribution of this work is the construction of a dataset of 120 hours of shot categories and news programs from Indian English news channels. We have experimented with SVM, HMM and CRF based classifiers and achieved a F1 score of 99% in detecting news programs while experimenting on our dataset.
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