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.

Subspace Clustering for Action Recognition with Covariance Representations and Temporal Pruning / Paoletti, Giancarlo; Cavazza, Jacopo; Beyan, Cigdem; Del Bue, Alessio. - (2020), pp. 6035-6042. (Intervento presentato al convegno ICPR tenutosi a Online (Milano) nel 10-15 January 2021).

Subspace Clustering for Action Recognition with Covariance Representations and Temporal Pruning

Beyan, Cigdem;
2020-01-01

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.
2020
25th International Conference on Pattern Recognition (ICPR)
New York
IAPR
Paoletti, Giancarlo; Cavazza, Jacopo; Beyan, Cigdem; Del Bue, Alessio
Subspace Clustering for Action Recognition with Covariance Representations and Temporal Pruning / Paoletti, Giancarlo; Cavazza, Jacopo; Beyan, Cigdem; Del Bue, Alessio. - (2020), pp. 6035-6042. (Intervento presentato al convegno ICPR tenutosi a Online (Milano) nel 10-15 January 2021).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/304319
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