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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9th International Conference on Intelligent Information Processing (IIP), Nov 2016, Melbourne, VIC, Australia. IFIP Advances in Information and Communication Technology, AICT-486, pp.121-131, 2016, Intelligent Information Processing VIII. 〈10.1007/978-3-319-48390-0_13〉
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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. IFIP Advances in Information and Communication Technology, AICT-486, pp.121-131, 2016, Intelligent Information Processing VIII. 〈10.1007/978-3-319-48390-0_13〉. 〈hal-01614985〉

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