Skip to Main content Skip to Navigation
Journal articles

3D Human Action Recognition by Shape Analysis of Motion Trajectories on Riemannian Manifold

Abstract : Recognizing human actions in 3D video sequences is an important open problem that is currently at the heart of many research domains including surveillance, natural interfaces and rehabilitation. However, the design and development of models for action recognition that are both accurate and efficient is a challenging task due to the variability of the human pose, clothing and appearance. In this paper, we propose a new framework to extract a compact representation of a human action captured through a depth sensor, and enable accurate action recognition. The proposed solution develops on fitting a human skeleton model to acquired data so as to represent the 3D coordinates of the joints and their change over time as a trajectory in a suitable action space. Thanks to such a 3D joint-based framework, the proposed solution is capable to capture both the shape and the dynamics of the human body simultaneously. The action recognition problem is then formulated as the problem of computing the similarity between the shape of trajectories in a Riemannian manifold. Classification using kNN is finally performed on this manifold taking advantage of Riemannian geometry in the open curve shape space. Experiments are carried out on four representative benchmarks to demonstrate the potential of the proposed solution in terms of accuracy/latency for a low-latency action recognition. Comparative results with state-of-the-art methods are reported.
Document type :
Journal articles
Complete list of metadata

Cited literature [39 references]  Display  Hide  Download
Contributor : Mohamed Daoudi Connect in order to contact the contributor
Submitted on : Wednesday, August 20, 2014 - 10:21:53 AM
Last modification on : Wednesday, January 19, 2022 - 2:42:02 PM
Long-term archiving on: : Tuesday, April 11, 2017 - 8:09:16 PM


Publisher files allowed on an open archive


  • HAL Id : hal-01056397, version 1


Maxime Devanne, Hazem Wannous, Stefano Berretti, Pietro Pala, Mohamed Daoudi, et al.. 3D Human Action Recognition by Shape Analysis of Motion Trajectories on Riemannian Manifold. IEEE Transactions on Cybernetics, IEEE, 2015, 45 (7), pp.1340-1352. ⟨hal-01056397⟩



Les métriques sont temporairement indisponibles