Name
Abstract | ||
|---|---|---|
AbstractWith the popularization of visual object tracking (VOT), more and more trajectory data are obtained and have begun to gain widespread attention in the fields of mobile robots, intelligent video surveillance, and the like. How to clean the anomalous trajectories hidden in the massive data has become one of the research hotspots. Anomalous trajectories should be detected and cleaned before the trajectory data can be effectively used. In this article, a Trajectory Evaluator by Sub-tracks (TES) for detecting VOT-based anomalous trajectory is proposed. Feature of Anomalousness is defined and described as the Eigenvector of classifier to filter Track Lets anomalous trajectory and IDentity Switch anomalous trajectory, which includes Feature of Anomalous Pose and Feature of Anomalous Sub-tracks (FAS). In the comparative experiments, TES achieves better results on different scenes than state-of-the-art methods. Moreover, FAS makes better performance than point flow, least square method fitting and Chebyshev Polynomial Fitting. It is verified that TES is more accurate and effective and is conducive to the sub-tracks trajectory data analysis. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3490032 | ACM Transactions on Knowledge Discovery from Data |
Keywords | DocType | Volume |
Data cleaning, data mining, trajectory analysis, anomalous trajectory detection | Journal | 16 |
Issue | ISSN | Citations |
4 | 1556-4681 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Gao | 1 | 18 | 9.17 |
| Jiada Li | 2 | 0 | 0.34 |
| Yisu Ge | 3 | 0 | 2.03 |
| Jianwen Shao | 4 | 0 | 0.34 |
| Shufang Lu | 5 | 0 | 0.34 |
| Libo Weng | 6 | 0 | 0.34 |