Context-aware learning for automatic sports highlight recognition

Video highlight recognition is the procedure in which a long video sequence is summarized into a shorter video clip that depicts the most “salient” parts of the sequence. It is an important technique for content delivery systems and search systems which create multimedia content tailored to their users' needs. This paper deals specifically with capturing highlights inherent to sports videos, especially for American football. Our proposed system exploits the multimodal nature of sports videos (i.e. visual, audio, and text cues) to detect the most important segments among them. The optimal combination of these cues is learned in a data-driven fashion using user preferences (expert input) as ground truth. Unlike most highlight recognition systems in the literature that define a highlight to be salient only in its own right (globally salient), we also consider the context of each video segment w.r.t. the video sequence it belongs to (locally salient). To validate our method, we compile a large dataset of broadcast American football videos, acquire their ground truth highlights, and evaluate the performance of our learning approach.