Self-Supervised Hierarchical Motion Learning for Egocentric Activity Recognition in Long Videos
Keywords:
egocentric activity recognition, self-supervised learning, hierarchical motion representation, long video understanding, system architecture, infrastructure, fairness, governance, sustainabilityAbstract
Egocentric activity recognition from long, uncurated video streams constitutes a foundational challenge for next-generation assistive technologies, augmented reality interfaces, and large-scale behavioral analytics. This paper presents a systems-level investigation into self-supervised hierarchical motion learning as a principled framework for addressing the temporal scale, viewpoint variability, and computational constraints inherent to egocentric video understanding. We conceptualize motion as a multiscale structural primitive and propose a hierarchical architecture that decomposes egocentric action into layered motion patterns, from fine-grained hand-object interactions to coarse daily routines. The self-supervised learning paradigm leverages the natural temporal coherence and cross-scale correlations of first-person footage, eliminating the prohibitive cost of dense manual annotation. Our discussion centers on system architecture, infrastructure trade-offs, and the integration of such models into real-world socio-technical pipelines. We analyze memory-compute co-design strategies for on-device and cloud-edge deployment, examine the robustness of learned representations under domain shift and egocentric sensor noise, and articulate the fairness risks embedded in training data distributions collected from narrow demographic profiles. Furthermore, we map out governance and policy implications, including the tension between behavioral inferencing accuracy and privacy preservation, and propose transparency mechanisms for model auditing. Through cross-domain comparisons with third-person video understanding systems and large-scale language-vision models, we highlight the unique structural demands of egocentric motion hierarchies. The paper concludes with a forward-looking agenda that integrates sustainability metrics, regulatory alignment, and participatory infrastructure design, arguing that hierarchical self-supervised motion learning is not merely a technical architecture but a bridge between low-level perception and ethically grounded, long-term activity intelligence.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
