Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks

23 May 2017  ·  Michael Gygli ·

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

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Camera shot boundary detection MSU Shot Boundary Detection Benchmark johmathe F score 0.7492 # 4
FPS 94 # 4