[1]KANG Wenxuan,CHEN Lifei,GUO Gongde.Spatiotemporal structure feature representation model for motion sequences[J].CAAI Transactions on Intelligent Systems,2023,18(2):240-250.[doi:10.11992/tis.202203011]
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Spatiotemporal structure feature representation model for motion sequences

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 2 Page number: 240-250 Column: 学术论文—机器学习 Public date: 2023-05-05
Title:
Spatiotemporal structure feature representation model for motion sequences
Author(s):
KANG Wenxuan1; 2;  CHEN Lifei1; 2; 3;  GUO Gongde1; 2; 3
1. College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350117, China;
2. Digital Fujian Internet-of-Things Laboratory of Environment Monitoring, Fuzhou 350117, China;
3. Center for Applied Mathematics of Fujian Province, Fuzhou 350117, China
Keywords:
motion sequence; multidimensional time series; feature extraction; spatiotemporal feature representation model; spatial transformation; key subsequence mining; event sequence; human activity recognition
CLC:
TP301.6
DOI:
10.11992/tis.202203011
Abstract:
The motion sequence is a multidimensional time series associated with motion signals, and there are high coupling characteristics between different dimensional sequences. Most of the existing multidimensional sequence representation methods are based on the assumption that the dimensions are independent or lack interpretability. As a result, we propose a spatiotemporal structure feature representation model for motion sequences and its two-stage construction method. First, the multidimensional time series are transformed into one-dimensional event sequences using the transformation method of spatially changing events, preserving the spatial structure characteristics of sequences. Second, an unsupervised mining algorithm for spatiotemporal structure features is defined. A collection of representative event grams of various durations and low redundancy are extracted from the event sequence as spatiotemporal structure features using the newly proposed representation measure. The experimental findings on various datasets for recognizing human behavior demonstrate that the new model’s feature set is more representative than the methods currently used to represent multidimensional time series, and it can considerably increase classification accuracy in the domain of motion sequence pattern recognition.
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