Papers › Subspace Clustering for Action Recognition with Covariance Representations and Temporal Pruning
Subspace Clustering for Action Recognition with Covariance Representations and Temporal Pruning
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.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Skeleton Based Action Recognition | Florence 3D | Temporal Spectral Clustering + Temporal Subspace Clustering | Accuracy | 95.81% | #4 of 7 | Archive leaderboard | report |
| Skeleton Based Action Recognition | Gaming 3D (G3D) | Temporal K-Means Clustering + Temporal Covariance Subspace Clustering | Accuracy | 92.91% | #2 of 4 | Archive leaderboard | report |
| Skeleton Based Action Recognition | HDM05 | Temporal Subspace Clustering | Accuracy | 89.80% | #1 of 1 | Archive leaderboard | report |
| Skeleton Based Action Recognition | MSR Action3D | Temporal K-Means Clustering + Temporal Subspace Clustering | Accuracy | 88.51% | #2 of 4 | Archive leaderboard | report |
| Skeleton Based Action Recognition | MSR ActionPairs | Temporal Subspace Clustering | Accuracy | 98.02% | #1 of 1 | Archive leaderboard | report |
| Skeleton Based Action Recognition | MSRC-12 | Temporal Subspace Clustering | Accuracy | 99.08% | #1 of 2 | Archive leaderboard | report |
| Skeleton Based Action Recognition | UT-Kinect | Temporal Subspace Clustering | Accuracy | 99.50% | #1 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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