Introduction
Figure 1: Two detected commonalities (white curves) projected on the corresponding distance matrix.
We present a novel solution to the problem of detecting common actions in time series of motion capture data and videos. Given two action sequences, our method discovers all pairs of similar subsequences, i.e. subsequences that represent the same action.
This is achieved in a completely unsupervised manner, i.e., without any prior knowledge of the type of actions, their number and their duration. These common subsequences (commonalities) may be located anywhere in the original sequences, may differ in duration and may be performed under different conditions e.g., by a different actor.
Methodology
The proposed method performs a very efficient graph-based search on the matrix of pairwise distances of frames of the two sequences. This search is supported by an objective function that captures the trade off between the similarity of the common subsequences and their lengths.
To discover all commonalities of two videos A, B MUCOS operates as follows:
Experiments - Downloads
Figure 2: Summary of the obtained results in all datasets..
Related Publications
[1] C. Panagiotakis, K. Papoutsakis and A.A. Argyros, A Graph-based Approach for Detecting Common Actions in
Motion Capture Data and Videos, Pattern Recognition, Pattern Recognition, Elsevier, vol. 79, pp. 1-11, July 2018.
[2] K. Papoutsakis, C. Panagiotakis and A.A. Argyros, "Temporal Action Co-Segmentation in 3D Motion Capture Data and Videos", In IEEE Computer Vision and Pattern Recognition (CVPR 2017), IEEE, Honolulu, Hawaii, USA, July 2017.