Adaptive Keyframe Selection for Video Summarization

The explosive growth of video data in the modern era has set the stage for research in the field of video summarization, which attempts to abstract the salient frames in a video in order to provide an easily interpreted synopsis. Existing work on video summarization has primarily been static - that is, the algorithms require the summary length to be specified as an input parameter. However, video streams are inherently dynamic in nature, while some of them are relatively simple in terms of visual content, others are much more complex due to camera/object motion, changing illumination, cluttered scenes and low quality. This necessitates the development of adaptive summarization techniques, which adapt to the complexity of a video and generate a summary accordingly. In this paper, we propose a novel algorithm to address this problem. We pose the summary selection as an optimization problem and derive an efficient technique to solve the summary length and the specific frames to be selected, through a single formulation. Our extensive empirical studies on a wide range of challenging, unconstrained videos demonstrate tremendous promise in using this method for real-world video summarization applications.