Robust Event Detection based on Spatio-Temporal Latent Action Unit using Skeletal Information
Hao Xing,Yuxuan Xue,Mingchuan Zhou,Darius Burschka,Hao Xing,Yuxuan Xue,Mingchuan Zhou,Darius Burschka
This paper proposes a novel dictionary learning approach to detect event anomalities using skeletal information extracted from RGBD video. The event action is represented as several latent action atoms and composed of latent spatial and temporal attributes. We aim to construct a network able to learn from few examples and also rules defined by the user. The skeleton frames are clustered by an init...
