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Detecting representative frames in videos based on human actions is quitechallenging because of the combined factors of human pose in action and thebackground. This paper addresses this problem and formulates the key framedetection as one of finding the video frames that optimally maximallycontribute to differentiating the underlying action category from all othercategories. To this end, we introduce a deep two-stream ConvNet for key framedetection in videos that learns to directly predict the location of key frames.Our key idea is to automatically generate labeled data for the CNN learningusing a supervised linear discriminant method. While the training data isgenerated taking many different human action videos into account, the trainedCNN can predict the importance of frames from a single video. We specify a newConvNet framework, consisting of a summarizer and discriminator. The summarizeris a two-stream ConvNet aimed at, first, capturing the appearance and motionfeatures of video frames, and then encoding the obtained appearance and motionfeatures for video representation. The discriminator is a fitting functionaimed at distinguishing between the key frames and others in the video. Weconduct experiments on a challenging human action dataset UCF101 and show thatour method can detect key frames with high accuracy.

Deep Keyframe Detection in Human Action Videos