Demo: Semantic Human Activity Annotation Tool Using Skeletonized Surveillance Videos

AbstractHuman activity data sets are fundamental for intelligent activity recognition in context-aware computing and intelligent video analysis. Surveillance videos include rich human activity data that are more realistic compared to data collected from a controlled environment. However, there are several challenges in annotating large data sets: 1) inappropriateness for crowd-sourcing because of public privacy, and 2) tediousness to manually select activities of people from busy scenes.

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