Re-thinking Human Activity Recognition with Hierarchy-aware Label Relationship Modeling

• URL: http://arxiv.org/abs/2403.05557v1
• Date: Sun, 11 Feb 2024 12:23:21 GMT
• Title: Re-thinking Human Activity Recognition with Hierarchy-aware Label Relationship Modeling
• Authors: Jingwei Zuo, Hakim Hacid,
• Abstract summary: Human Activity Recognition (HAR) has been studied for decades, from data collection, learning models, to post-processing and result interpretations. In this paper, we propose H-HAR, by rethinking the HAR tasks from a fresh perspective by delving into their intricate global label relationships. Being hierarchy-aware, the graph-based label modeling enhances the fundamental HAR model, by incorporating intricate label relationships into the model.
• Score: 1.2277343096128712
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human Activity Recognition (HAR) has been studied for decades, from data collection, learning models, to post-processing and result interpretations. However, the inherent hierarchy in the activities remains relatively under-explored, despite its significant impact on model performance and interpretation. In this paper, we propose H-HAR, by rethinking the HAR tasks from a fresh perspective by delving into their intricate global label relationships. Rather than building multiple classifiers separately for multi-layered activities, we explore the efficacy of a flat model enhanced with graph-based label relationship modeling. Being hierarchy-aware, the graph-based label modeling enhances the fundamental HAR model, by incorporating intricate label relationships into the model. We validate the proposal with a multi-label classifier on complex human activity data. The results highlight the advantages of the proposal, which can be vertically integrated into advanced HAR models to further enhance their performances.

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