Real-World Graph Convolution Networks (RW-GCNs) for Action Recognition in Smart Video Surveillance
Justin Sanchez,
Christopher Neff,
Hamed Tabkhi
Abstract:Action recognition is a key algorithmic part of emerging on-the-edge smart video surveillance and security systems. Skeleton-based action recognition is an attractive approach which, instead of using RGB pixel data, relies on human pose information to classify appropriate actions. However, existing algorithms often assume ideal conditions that are not representative of real-world limitations, such as noisy input, latency requirements, and edge resource constraints.To address the limitations of existing approac… Show more
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