Name
Abstract | ||
|---|---|---|
Trajectory classification has been extensively investigated in recent years, however, problems remain when processing incomplete trajectories of noises and local variations. In this paper, we propose a Locality-constrained Sparse Reconstruction (LSR) approach that explores both sparsity and local adaptability for robust trajectory classification. A trajectory dictionary with locality constrains is constructed with track lets partitioned from collected trajectories by control points of cubic B-spline curves. On the dictionary, the proposed LSR is used to calculate a discriminate code matrix. Then, a loss weighted decoding strategy is employed to perform multi-class trajectory classification. In addition, the approach can be used for anomalous trajectory detection with a thresholding strategy. Experiments on two datasets show that the results of the LSR approach improve the state of the art. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICPR.2014.449 | ICPR |
Keywords | DocType | ISSN |
trajectory dictionary,locality constrains,discriminate code matrix,matrix algebra,cubic b-spline curves,image reconstruction,locality-constrained sparse reconstruction,image classification,multiclass trajectory classification,loss weighted decoding strategy,robust trajectory classification,splines (mathematics),thresholding strategy | Conference | 1051-4651 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ce Li | 1 | 37 | 8.03 |
| Zhenjun Han | 2 | 176 | 16.40 |
| Qixiang Ye | 3 | 913 | 64.51 |
| Shan Gao | 4 | 1 | 1.13 |
| Lijin Pang | 5 | 0 | 0.34 |
| Jianbin Jiao | 6 | 367 | 32.61 |