[1]LU Jianfeng,GUO Maozu,ZHANG Yu,et al.Stay point recognition method based on spatio-temporal constraint density clustering[J].CAAI Transactions on Intelligent Systems,2020,15(1):59-66.[doi:10.11992/tis.201910026]
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Stay point recognition method based on spatio-temporal constraint density clustering
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
15
Number of periods:
2020 1
Page number:
59-66
Column:
学术论文—智能系统
Public date:
2020-01-05
- Title:
- Stay point recognition method based on spatio-temporal constraint density clustering
- Keywords:
- stay point identification; density clustering; space-time constraint; indirect spatio-temporal feature; spatio-temporal similaily; aggregatied; process uniformity; fine-grained
- CLC:
- TP301
- DOI:
- 10.11992/tis.201910026
- Abstract:
- The recognition of the track stay point is the key to the trajectory analysis and the semantic mining of travel activities. Aiming at the defect of spatio-temporal information based on density clustering, the new method of space-time constrained stay point recognition is proposed. In the density clustering, the indirect spatio-temporal feature representation of the trajectory is introduced, and the trajectory points with spatio-temporal similarity are aggregated. The spatio-temporal feature constraint unified with the clustering process is used to fine grain the trajectory cluster. Therefore, when the constraints are used, the input data features used in the clustering are reused, and the full utilization of the features improves accuracy of the recognition. The experimental results verify effectiveness of the proposed method.
- References:
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