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A probabilistic Bayesian belief network (BBN) based framework is proposed for semantic analysis and summarization of video using event detection. Our approach is customized for soccer but can be applied to other types of sports video sequences. We extract excitement clips from soccer sports video sequences that are comprised of multiple subclips corresponding to the events such as replay, field-view,...
In this paper, we present a novel hierarchical framework and effective algorithms for cricket event detection and classification. The proposed scheme performs a topdown video event detection and classification using hierarchical tree which avoids shot detection and clustering. In the hierarchy, at level-1, we use audio features, to extract excitement clips from the cricket video. At level-2, we classify...
A novel framework is presented for semantic labeling of video clips, automatically segmented from broadcast video of soccer (football) games, as highlights and excitement clips etc. The proposed framework provides a generalizable method for linking low-level video features with high- level semantic concepts defined in a commonly understood sports lexicon. Three important contributions are made to...
Tracking and recognition of objects in video sequences suffer from difficulties in learning appropriate object models. Often a high degree of supervision is required, including manual annotation of many training images. We aim at unsupervised learning of object models and present a novel way to build models based on motion information extracted from video sequences. We require a coarse delineation...
In this paper, we propose a system that semantically classifies news video at different layers of semantic significance, using different elements of visual content. The classification hierarchy generates low-level concepts, and concept hierarchy generates high-level concepts from low-level concepts. Our classification hierarchy is based on a few popular audio and video content analysis techniques...
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