Trajectory Pattern Identification and Anomaly Detection of Pedestrian Flows Based on Visual Clustering
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.
KeywordsData mining Pedestrian trajectory pattern Visualization Clustering iVAT algorithm
This work is partially supported by China Scholarship Council. The authors want to acknowledge Mr. Xiaoting Wang for his suggestions on our works and the anonymous reviewers for their constructive suggestions and feedback.
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