Gygli Michael

Abstract

Shot boundary detection (SBD) is an important component of many videoanalysis tasks, such as action recognition, video indexing, summarization andediting. Previous work typically used a combination of low-level features likecolor histograms, in conjunction with simple models such as SVMs. Instead, wepropose to learn shot detection end-to-end, from pixels to final shotboundaries. For training such a model, we rely on our insight that all shotboundaries are generated. Thus, we create a dataset with one million frames andautomatically generated transitions such as cuts, dissolves and fades. In orderto efficiently analyze hours of videos, we propose a Convolutional NeuralNetwork (CNN) which is fully convolutional in time, thus allowing to use alarge temporal context without the need to repeatedly processing frames. Withthis architecture our method obtains state-of-the-art results while running atan unprecedented speed of more than 120x real-time.