We present a solution to the problem of discovering all periodic
segments of a video and of estimating their period in
a completely unsupervised manner. These segments may be
located anywhere in the video, may differ in duration, speed,
period and may represent unseen motion patterns of any type
of objects (e.g., humans, animals, machines, etc). The proposed
method capitalizes on earlier research on the problem
of detecting common actions in videos, also known as commonality
detection or video co-segmentation. The proposed
method has been evaluated quantitatively and in comparison
to a baseline, power-spectrum-based approach, on two
ground-truth-annotated datasets (MHAD202-v, PERTUBE).
From those, PERTUBE has been compiled specifically for
the purposes of this study and includes a collection of youtube
videos that have been shot in the wild, with several periodic
segments. The results of this evaluation demonstrate that
the propose method outperforms the baseline considerably,
especially in the more challenging PERTUBE dataset.