Demo: Semantic Human Activity Annotation Tool Using Skeletonized Surveillance Videos

ACM International Joint Conference on Pervasive and Ubiquitous Computing
2019

Abstract

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.

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