[1]GUO Maozu,SHAO Shoufei,ZHAO Lingling,et al.Active semantic recognition method based on spatial-temporal period pattern mining[J].CAAI Transactions on Intelligent Systems,2021,16(1):162-169.[doi:10.11992/tis.202012035]
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Active semantic recognition method based on spatial-temporal period pattern mining

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 1 Page number: 162-169 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2021-01-05
Title:
Active semantic recognition method based on spatial-temporal period pattern mining
Author(s):
GUO Maozu1; 2;  SHAO Shoufei1; 2;  ZHAO Lingling3;  LI Yang1; 2
1. School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China;
2. Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing University of Civil Engineering and
Keywords:
spatial-temporal trajectory; spatial-temporal close connection;  density clustering;  stay time;  active semantic recognition; period pattern mining; random forest
CLC:
TP181
DOI:
10.11992/tis.202012035
Abstract:
Active semantic recognition aims to mine people’s activities from spatial-temporal data recording through the smart equipment they carry. Traditional studies paid more attention to studying the spatial features of spatial-temporal data but failed to mine temporal features adequately. Considering both features, this work proposes an active semantic recognition method based on period pattern mining. First, trajectories that have already been separated from raw trajectories are clustered based on the spatial distance. The periods of reference spots that are frequently visited by the people are then mined according to the sequence of clustering. Based on the visit period and combined with the residence time at the location and the distribution of interest points nearby, a classification model is constructed to identify the activity semantics of human individuals. The experimental results on the check-in dataset and simulation data show that the valid recognition accuracy of active semantic recognition combined with periodic characteristics increases by 20% more than that without periodic characteristics. Under the same two check-in datasets and compared with other recognition methods, the accuracy is improved by more than 10%.
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