Automatic Selection of Keyframes for Activity Recognition

Recognizing activities in image sequences is an open problem in computer vision. In this paper we present a method to extract the most significant frames from an activity sequence. We name these frames as the keyframes. Moreover, we describe a pre-processing stage in order to build a robust representation for different human movements. Using this representation, we build an activity eigenspace that is used to obtain a probability measure. We use this measure to develop a method to select the activity keyframes automatically.

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