Trajectory Pattern Identification and Anomaly Detection of Pedestrian Flows Based on Visual Clustering

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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [10 references]  Display  Hide  Download

https://hal.inria.fr/hal-01614985
Contributor : Hal Ifip <>
Submitted on : Wednesday, October 11, 2017 - 4:57:35 PM
Last modification on : Wednesday, October 11, 2017 - 5:00:33 PM
Long-term archiving on : Friday, January 12, 2018 - 3:47:21 PM

File

433802_1_En_13_Chapter.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Li Li, Christopher Leckie. Trajectory Pattern Identification and Anomaly Detection of Pedestrian Flows Based on Visual Clustering. 9th International Conference on Intelligent Information Processing (IIP), Nov 2016, Melbourne, VIC, Australia. pp.121-131, ⟨10.1007/978-3-319-48390-0_13⟩. ⟨hal-01614985⟩

Share

Metrics

Record views

109

Files downloads

93