arXiv:2607.00716v1 Announce Type: cross Abstract: Skeleton-based action recognition has achieved remarkable success by exploiting joint coordinates and their topological connections, yet prevailing methods overwhelmingly assume complete and clean skeleton inputs. In real-world deployments, such as egocentric vision, crowded surveillance, wearable devices, or edge robotics, limited field-of-view (FoV) frequently causes substantial joint visibility dropout, leading to severe performance degradation that existing models are largely unprepared to handle. To bridge this critical yet underexplored g
The proliferation of AI in real-world, dynamic environments necessitates robust action recognition capabilities despite challenging, incomplete data inputs.
Improving action recognition under partial visibility is crucial for deploying AI reliably in diverse applications like robotics, surveillance, and wearable tech, enhancing their effectiveness and safety.
This research addresses a fundamental limitation in skeleton-based action recognition, potentially leading to more resilient and adaptable AI systems in unpredictable environments.
- · Robotics industry
- · Surveillance technology providers
- · Wearable device manufacturers
- · AI algorithm developers
- · Systems highly dependent on complete, clean data
- · AI models without robustness to partial observations
AI systems can interpret human actions more effectively in complex, real-world settings with occlusions.
This improved reliability accelerates the adoption of AI in safety-critical applications where human-robot interaction is common.
Enhanced action recognition contributes to sophisticated AI agents capable of understanding and anticipating human intent in collaborative or assistive roles.
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Read at arXiv cs.AI