A Moving Pattern Classification Based on Multimodal Data for Public Safety Services
Eunjung Kwon, WonJae Shin, Hyunho Park, Sungwon Byon, Eui‐Suk Jung, Yong-Tae Lee, Kyu-Chul Lee · 2020
The location based services using logged data of having people's moving traces and activity patterns has been used in the real world today. While a number of models for predicting the next visiting position information by users or the class labels of moving objects has been rapidly adopted in service areas such as traffic management, public safety in order to maximize their requirements, There is the difficulty of developing feature compositions with low-dimensional and heterogeneous feature space. To address these issues, this paper proposes a movement classification model that can classify people's movement paths according to their point of interests that is predicted by our proposed method.