376 research outputs found
Persistent Homology of Attractors For Action Recognition
In this paper, we propose a novel framework for dynamical analysis of human
actions from 3D motion capture data using topological data analysis. We model
human actions using the topological features of the attractor of the dynamical
system. We reconstruct the phase-space of time series corresponding to actions
using time-delay embedding, and compute the persistent homology of the
phase-space reconstruction. In order to better represent the topological
properties of the phase-space, we incorporate the temporal adjacency
information when computing the homology groups. The persistence of these
homology groups encoded using persistence diagrams are used as features for the
actions. Our experiments with action recognition using these features
demonstrate that the proposed approach outperforms other baseline methods.Comment: 5 pages, Under review in International Conference on Image Processin
Topological signature for periodic motion recognition
In this paper, we present an algorithm that computes the topological
signature for a given periodic motion sequence. Such signature consists of a
vector obtained by persistent homology which captures the topological and
geometric changes of the object that models the motion. Two topological
signatures are compared simply by the angle between the corresponding vectors.
With respect to gait recognition, we have tested our method using only the
lowest fourth part of the body's silhouette. In this way, the impact of
variations in the upper part of the body, which are very frequent in real
scenarios, decreases considerably. We have also tested our method using other
periodic motions such as running or jumping. Finally, we formally prove that
our method is robust to small perturbations in the input data and does not
depend on the number of periods contained in the periodic motion sequence.Comment: arXiv admin note: substantial text overlap with arXiv:1707.0698
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