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A compact and recursive Riemannian motion descriptor for untrimmed activity recognition

Abstract : A very low dimension frame-level motion descriptor is herein proposed with the capability to represent incomplete dynamics, thus allowing online action prediction. At each frame, a set of local trajectory kinematic cues are spatially pooled using a covariance matrix. The set of frame-level covariance matrices forms a Riemannian manifold that describes motion patterns. A set of statistic measures are computed over this manifold to characterize the sequence dynamics, either globally, or instantaneously from a motion history. Regarding the Riemannian metrics, two different versions are proposed: (1) by considering tangent projections with respect to updated recursive statistics, and (2) by mapping the covariance onto a linear matrix using as reference the identity matrix. The proposed approach was evaluated for two different tasks: (1) for action classification on complete video sequences and (2) for online action recognition, in which the activity is predicted at each frame. The method was evaluated using two public datasets: KTH and UT-interaction. For action classification, the method achieved an average accuracy of 92.27 and 81.67%, for KTH and UT-interaction, respectively. In partial recognition task, the proposed method achieved similar classification rate as for the whole sequence using only the 40 and 70% on KTH and UT sequences, respectively.
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https://hal.archives-ouvertes.fr/hal-03204019
Contributeur : Antoine Manzanera Connectez-vous pour contacter le contributeur
Soumis le : mercredi 21 avril 2021 - 11:31:15
Dernière modification le : lundi 3 mai 2021 - 10:55:44
Archivage à long terme le : : jeudi 22 juillet 2021 - 18:30:58

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Fabio Martinez Carrillo, Michèle Gouiffès, Gustavo Garzon Villamizar, Antoine Manzanera. A compact and recursive Riemannian motion descriptor for untrimmed activity recognition. Journal of Real-Time Image Processing, Springer Verlag, inPress, ⟨10.1007/s11554-020-01057-9⟩. ⟨hal-03204019⟩

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