Subspace Clustering for Action Recognition with Covariance Representations and Temporal Pruning

21 Jun 2020  ·  Giancarlo Paoletti, Jacopo Cavazza, Cigdem Beyan, Alessio Del Bue ·

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. read more

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Skeleton Based Action Recognition Florence 3D Temporal Spectral Clustering + Temporal Subspace Clustering Accuracy 95.81% # 3
Skeleton Based Action Recognition Gaming 3D (G3D) Temporal K-Means Clustering + Temporal Covariance Subspace Clustering Accuracy 92.91% # 2
Skeleton Based Action Recognition HDM05 Temporal Subspace Clustering Accuracy 89.80% # 1
Skeleton Based Action Recognition MSR Action3D Temporal K-Means Clustering + Temporal Subspace Clustering Accuracy 88.51% # 1
Skeleton Based Action Recognition MSR ActionPairs Temporal Subspace Clustering Accuracy 98.02% # 1
Skeleton Based Action Recognition MSRC-12 Temporal Subspace Clustering Accuracy 99.08% # 1
Skeleton Based Action Recognition UT-Kinect Temporal Subspace Clustering Accuracy 99.50% # 1


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