Subspace Clustering for Action Recognition with Covariance Representations and Temporal Pruning

• URL: http://arxiv.org/abs/2006.11812v1
• Date: Sun, 21 Jun 2020 14:44:03 GMT
• Title: Subspace Clustering for Action Recognition with Covariance Representations and Temporal Pruning
• Authors: Giancarlo Paoletti, Jacopo Cavazza, Cigdem Beyan and Alessio Del Bue
• Abstract summary: This paper tackles the problem of human action recognition, defined as classifying which action is displayed in a trimmed sequence, from skeletal data. We propose a novel subspace clustering method, which exploits covariance matrix to enhance the action's discriminability and a timestamp pruning approach that allow us to better handle the temporal dimension of the data.
• Score: 20.748083855677816
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper tackles the problem of human action recognition, defined as classifying which action is displayed in a trimmed sequence, from skeletal data. Albeit state-of-the-art approaches designed for this application are all supervised, in this paper we pursue a more challenging direction: Solving the problem with unsupervised learning. To this end, we propose a novel subspace clustering method, which exploits covariance matrix to enhance the action's discriminability and a timestamp pruning approach that allow us to better handle the temporal dimension of the data. Through a broad experimental validation, we show that our computational pipeline surpasses existing unsupervised approaches but also can result in favorable performances as compared to supervised methods.

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