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A periodicity detection dataset

Dataset specifications

Both datasets, that are introduced in this paper, have been evaluated on P-MUCOS method and the results are available on the paper.

We present a solution to the problem of discovering all temporally periodic segments of a given video and of estimating their period in a completely unsupervised manner. These periodic segments may be located anywhere in the original sequence, 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 [1-2].

Visualization of the matrix D of pairwise distances of the video frames, in which two periodic motions (jumping, waiving) appear after a non-periodic one (stand up). Warm (cold) colors correspond to high (low) values in D. Periodic segments are manifested as straight line segments in D (in white) that are parallel to the main diagonal and are associated with low sum of D values.

Related Publications

  • [1] C. Panagiotakis, K. Papoutsakis and A. Argyros, A Graph-based Approach for Detecting Common Actions in Motion Capture Data and Videos, Pattern Recognition, 2017.
  • [2] K. Papoutsakis, C. Panagiotakis and A.A. Argyros, "Temporal Action Co-Segmentation in 3D Motion Capture Data and Videos", CVPR 2017