Deep Keyframe Detection in Human Action Videos

Understand

Detecting representative frames in videos based on human actions is quite challenging because of the combined factors of human pose in action and the background.

• This paper addresses this problem and formulates the key frame detection as one of finding the video frames that optimally maximally contribute to differentiating the underlying action category from all other categories.
• To this end, we introduce a deep two-stream ConvNet for key frame detection in videos that learns to directly predict the location of key frames.
• Our key idea is to automatically generate labeled data for the CNN learning using a supervised linear discriminant method.