A graph-based approach for detecting common actions in motion capture data and videos

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 common subsequences, i.e. subsequences that represent the same or similar 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. 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. The proposed method has been evaluated quantitatively on challenging datasets and in comparison to state of the art approaches. The obtained results demonstrate that the proposed method outperforms the state of the art methods both in the quality of the obtained solutions and in computational performance.