  We address the problem of temporal localization of repetitive activities in a
video, i.e., the problem of identifying all segments of a video that contain
some sort of repetitive or periodic motion. To do so, the proposed method
represents a video by the matrix of pairwise frame distances. These distances
are computed on frame representations obtained with a convolutional neural
network. On top of this representation, we design, implement and evaluate
ReActNet, a lightweight convolutional neural network that classifies a given
frame as belonging (or not) to a repetitive video segment. An important
property of the employed representation is that it can handle repetitive
segments of arbitrary number and duration. Furthermore, the proposed training
process requires a relatively small number of annotated videos. Our method
raises several of the limiting assumptions of existing approaches regarding the
contents of the video and the types of the observed repetitive activities.
Experimental results on recent, publicly available datasets validate our design
choices, verify the generalization potential of ReActNet and demonstrate its
superior performance in comparison to the current state of the art.
