# Histogram of Oriented Principal Components for Cross-View Action Recognition

arXiv: https://arxiv.org/abs/1409.6813 · HTML: https://mlsanity.com/paper/1409.6813/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2014.

## Built on by (most cited first, 27 of 27)

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- [MECCANO: A Multimodal Egocentric Dataset for Humans Behavior Understanding in the Industrial-like Domain](https://mlsanity.com/paper/2209.08691/) (2022)
- [Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognition](https://mlsanity.com/paper/2406.13327/) (2024)
- [Temporal-Viewpoint Transportation Plan for Skeletal Few-shot Action Recognition](https://mlsanity.com/paper/2210.16820/) (2022)
- [3D Skeleton-based Few-shot Action Recognition with JEANIE is not so Na"ive](https://mlsanity.com/paper/2112.12668/) (2021)
- [Unsupervised Human Action Recognition with Skeletal Graph Laplacian and Self-Supervised Viewpoints Invariance](https://mlsanity.com/paper/2204.10312/) (2022)
- [Meet JEANIE: a Similarity Measure for 3D Skeleton Sequences via Temporal-Viewpoint Alignment](https://mlsanity.com/paper/2402.04599/) (2024)
- [Analysis and Evaluation of Kinect-based Action Recognition Algorithms](https://mlsanity.com/paper/2112.08626/) (2021)
- [Computer Vision for Clinical Gait Analysis: A Gait Abnormality Video Dataset](https://mlsanity.com/paper/2407.04190/) (2024)
- [Video-based Human Action Recognition using Deep Learning: A Review](https://mlsanity.com/paper/2208.03775/) (2022)
- [Deep Learning for Computer Vision based Activity Recognition and Fall Detection of the Elderly: a Systematic Review](https://mlsanity.com/paper/2401.11790/) (2024)
- [3D Skeleton-Based Action Recognition: A Review](https://mlsanity.com/paper/2506.00915/) (2025)
- [Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and Beyond](https://mlsanity.com/paper/2406.02978/) (2024)
- [HAA4D: Few-Shot Human Atomic Action Recognition via 3D Spatio-Temporal Skeletal Alignment](https://mlsanity.com/paper/2202.07308/) (2022)
- [When Spatial meets Temporal in Action Recognition](https://mlsanity.com/paper/2411.15284/) (2024)
- [SDFA: Structure Aware Discriminative Feature Aggregation for Efficient Human Fall Detection in Video](https://mlsanity.com/paper/2503.07008/) (2025)
- [TS-RGBD Dataset: a Novel Dataset for Theatre Scenes Description for People with Visual Impairments](https://mlsanity.com/paper/2308.01035/) (2023)
- [Self-supervised Multi-view Clustering in Computer Vision: A Survey](https://mlsanity.com/paper/2309.09473/) (2023)
- [Progressive Modality Cooperation for Multi-Modality Domain Adaptation](https://mlsanity.com/paper/2506.19316/) (2025)
- [Learning Scene Flow With Skeleton Guidance For 3D Action Recognition](https://mlsanity.com/paper/2306.13285/) (2023)
- [Group Activity Recognition in Computer Vision: A Comprehensive Review, Challenges, and Future Perspectives](https://mlsanity.com/paper/2307.13541/) (2023)
- [An Improved Graph Pooling Network for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2404.16359/) (2024)
- [Modeling Human Skeleton Joint Dynamics for Fall Detection](https://mlsanity.com/paper/2503.06938/) (2025)
- [Lens Privacy Sealing: A New Benchmark and Method for Physical Privacy-Preserving Action Recognition](https://mlsanity.com/paper/2605.19578/) (2026)

## Abstract (arXiv)

Existing techniques for 3D action recognition are sensitive to viewpoint variations because they extract features from depth images which are viewpoint dependent. In contrast, we directly process pointclouds for cross-view action recognition from unknown and unseen views. We propose the Histogram of Oriented Principal Components (HOPC) descriptor that is robust to noise, viewpoint, scale and action speed variations. At a 3D point, HOPC is computed by projecting the three scaled eigenvectors of the pointcloud within its local spatio-temporal support volume onto the vertices of a regular dodecahedron. HOPC is also used for the detection of Spatio-Temporal Keypoints (STK) in 3D pointcloud sequences so that view-invariant STK descriptors (or Local HOPC descriptors) at these key locations only are used for action recognition. We also propose a global descriptor computed from the normalized spatio-temporal distribution of STKs in 4-D, which we refer to as STK-D. We have evaluated the performance of our proposed descriptors against nine existing techniques on two cross-view and three single-view human action recognition datasets. The Experimental results show that our techniques provide significant improvement over state-of-the-art methods.