Exploring Self-supervised Skeleton-based Action Recognition in Occluded Environments

Yifei Chen; Kunyu Peng; Alina Roitberg; David Schneider; Jiaming Zhang; Junwei Zheng; Yufan Chen; Ruiping Liu; Kailun Yang; Rainer Stiefelhagen

arXiv · 2309.12029

Exploring Self-supervised Skeleton-based Action Recognition in Occluded Environments

Yifei Chen·Kunyu Peng·Alina Roitberg·David Schneider·Jiaming Zhang·Junwei Zheng·Yufan Chen·Ruiping Liu·Kailun Yang·Rainer Stiefelhagen

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Abstract

To integrate action recognition into autonomous robotic systems, it is essential to address challenges such as person occlusions-a common yet often overlooked scenario in existing self-supervised skeleton-based action recognition methods. In this work, we propose IosPSTL, a simple and effective self-supervised learning framework designed to handle occlusions. IosPSTL combines a cluster-agnostic KNN imputer with an Occluded Partial Spatio-Temporal Learning (OPSTL) strategy. First, we pre-train the model on occluded skeleton sequences. Then, we introduce a cluster-agnostic KNN imputer that performs semantic grouping using k-means clustering on sequence embeddings. It imputes missing skeleton data by applying K-Nearest Neighbors in the latent space, leveraging nearby sample representations to restore occluded joints. This imputation generates more complete skeleton sequences, which significantly benefits downstream self-supervised models. To further enhance learning, the OPSTL module incorporates Adaptive Spatial Masking (ASM) to make better use of intact, high-quality skeleton sequences during training. Our method achieves state-of-the-art performance on the occluded versions of the NTU-60 and NTU-120 datasets, demonstrating its robustness and effectiveness under challenging conditions. Code is available at https://github.com/cyfml/OPSTL.

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BibTeX RIS

Yifei Chen, Kunyu Peng, Alina Roitberg, David Schneider, Jiaming Zhang, Junwei Zheng, Yufan Chen, Ruiping Liu, Kailun Yang, Rainer Stiefelhagen. 2025-04-16. Exploring Self-supervised Skeleton-based Action Recognition in Occluded Environments. https://arxiv.org/abs/2309.12029

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