Semantic human activity annotation tool using skeletonized surveillance videos
Bokyung Lee, Michael Lee, Pan Zhang, Alexander Tessier, Azam Khan · 2019
Human 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.