Name
Abstract | ||
|---|---|---|
Useru0027s position and trajectory are very important personal informations, based on which, the useru0027s personalized description can be realized, and the useru0027s intentions, preferences and behavior patterns can also be found. For most of traditional approaches, the trajectory learning problems assume the availability of a vast amount of labeled training data, which requires a great deal of manual effort. In this paper we propose a indoor trajectory restoration method based on the spatial constraints on the position of points of interests (PoI). This method discovers PoI position by detecting the status changing of furniture and facilities and constructs spatial relationship of PoI positions with multidimensional scaling (MDS). Then, it synthetically optimizes the location model constructing of fitting error to labeled points, the manifold regularization of high-dimensional signal strength vectors and the MDS constraint of the spatial relationship of PoI positions, to achieve the optimizing trajectory by iteration. The experimental results show that our proposed method can effectively improve the robustness of the trajectory learning ability in different practical scenarios, benefiting from the constraint of PoI position. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00118 | SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinlong Jiang | 1 | 76 | 10.70 |
| Yunbing Xing | 2 | 0 | 0.34 |
| Yiqiang Chen | 3 | 1446 | 109.32 |
| Yang Gu | 4 | 56 | 7.54 |
| Jun-Fa Liu | 5 | 0 | 0.68 |