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 analys
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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. Keywords: Data mining · Pedestrian trajectory pattern tion · Clustering · iVAT algorithm
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Introduction
There is growing interest in the problem of extracting useful information from massive trajectory datasets derived by various sensing methods. Understanding patterns of pedestrian movement is useful in applications such as pedestrian flow management, public security and safety. A major challenge in pattern analysis of pedestrian movement is how to discover and describe the movement patterns hidden in trajectories, and identify any misbehaviour or interesting events. The main approaches to trajectory data analysis and anomaly detection fall into the category of trajectory data mining. To detect and recognize social events, two common approaches used to address this problem are statistical methods combined with classification and clustering-based methods. Many existing approaches to address this problem have the limitations that they focus on the c IFIP International Federation for Information Processing 2016 Published by Springer International Publishing AG 2016. All Rights Reserved Z. Shi et al. (Eds.): IIP 2016, IFIP AICT 486, pp. 121–131, 2016. DOI: 10.1007/978-3-319-48390-0 13
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details of trajectories, but do not consider the characteristics of the trajectory distribution. In this paper, we address this limitation of existing approaches by proposing the use of contour maps and visual clustering. Contour maps are a very useful visualization tool for three-dimensional data, which we adopt to visually describe the connection between different subareas and describe the distribution of trajectories. Visual clustering methods such as VAT (Visual Assessment of cluster Tendency) and iVAT (improved Visual Assessment of cluster Tendency) [1,9] are proposed to visually assess the clustering tendency of a set of objects. By using the VAT/iVAT approach, we are able to visualize and determine the possible number of clusters of locations or the periods with similar activity, and then determine abnormal areas/days with significantly different trajectory distributio
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