Unsupervised Detection of Periodic Segments in Videos
Costas Panagiotakis, Giorgos Karvounas, Antonis Argyros · 2018
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 you tube 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.