Exploring Self-Supervised Skeleton-Based Human Action Recognition under Occlusions

• URL: http://arxiv.org/abs/2309.12029v2
• Date: Wed, 23 Oct 2024 04:36:26 GMT
• Title: Exploring Self-Supervised Skeleton-Based Human Action Recognition under Occlusions
• Authors: Yifei Chen, Kunyu Peng, Alina Roitberg, David Schneider, Jiaming Zhang, Junwei Zheng, Ruiping Liu, Yufan Chen, Kailun Yang, Rainer Stiefelhagen,
• Abstract summary: We propose a method to integrate self-supervised skeleton-based action recognition methods into autonomous robotic systems. We first pre-train using occluded skeleton sequences, then use k-means clustering (KMeans) on sequence embeddings to group semantically similar samples. Imputing incomplete skeleton sequences to create relatively complete sequences provides significant benefits to existing skeleton-based self-supervised methods.
• Score: 40.322770236718775
• License:
• Abstract: To integrate self-supervised skeleton-based action recognition methods into autonomous robotic systems, it is crucial to consider adverse situations involving target occlusions. Such a scenario, despite its practical relevance, is rarely addressed in existing self-supervised skeleton-based action recognition methods. To empower models with the capacity to address occlusion, we propose a simple and effective method. We first pre-train using occluded skeleton sequences, then use k-means clustering (KMeans) on sequence embeddings to group semantically similar samples. Next, we propose KNN-Imputation to fill in missing skeleton data based on the closest sample neighbors. Imputing incomplete skeleton sequences to create relatively complete sequences as input provides significant benefits to existing skeleton-based self-supervised methods. Meanwhile, building on the state-of-the-art Partial Spatio-Temporal Learning (PSTL), we introduce an Occluded Partial Spatio-Temporal Learning (OPSTL) framework. This enhancement utilizes Adaptive Spatial Masking (ASM) for better use of high-quality, intact skeletons. The new proposed method is verified on the challenging occluded versions of the NTURGB+D 60 and NTURGB+D 120. The source code is publicly available at https://github.com/cyfml/OPSTL.

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