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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.
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Submitted on : Wednesday, October 11, 2017 - 4:57:35 PM
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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⟩



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