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Trajectory Pattern Identification and Anomaly Detection of Pedestrian Flows Based on Visual Clustering
| Content Provider | Hyper Articles en Ligne (HAL) |
|---|---|
| Author | Li, Li Leckie, Christopher |
| Abstract | Extracting pedestrian movement patterns and determining anomalous regions/time periods is a major challenge in data mining of massive trajectory datasets. In this paper, we apply contour map and visual clustering algorithms to visually identify and analyse areas/time periods with anomalous distributions of pedestrian flows. Contour maps are adopted as the visualization method of the origin-destination flow matrix to describe the distribution of pedestrian movement in terms of entry/exit areas. By transforming the origin-destination flow matrix into a dissimilarity matrix, the iVAT visual clustering algorithm is applied to visually cluster the most popular and related areas. A novel method based on the iVAT algorithm is proposed to detect normal/abnormal time periods with similar/anomalous pedestrian flow patterns. Synthetic and large, real-life datasets are used to validate the effectiveness of our proposed algorithms. |
| Ending Page | 131 |
| Page Count | 11 |
| Starting Page | 121 |
| File Format | |
| DOI | 10.1007/978-3-319-48390-0_13 |
| Language | English |
| Publisher Date | 2016-01-01 |
| Access Restriction | Open |
| Subject Keyword | Data mining Pedestrian trajectory pattern Visualization Clustering iVAT algorithm info Computer Science [cs] |
| Content Type | Text |
| Resource Type | Article |
