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Entropy 2017, 19(7), 323; https://doi.org/10.3390/e19070323

Trajectory Shape Analysis and Anomaly Detection Utilizing Information Theory Tools

1,2
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1,* , 1
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1,2,* and 1
1
School of Computer Science and Technology, Tianjin University, Yaguan Road #135, Tianjin 300350, China
2
Graphics and Imaging Lab, Universitat de Girona, Campus Montilivi, 17071 Girona, Spain
This paper is an extended version of our paper published in 22nd International Conference on Neural Information Processing, Istanbul, Turkey, 9–12 November 2015, pp. 423–431.
*
Authors to whom correspondence should be addressed.
Received: 15 February 2017 / Revised: 9 June 2017 / Accepted: 27 June 2017 / Published: 30 June 2017
(This article belongs to the Section Information Theory)
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Abstract

In this paper, we propose to improve trajectory shape analysis by explicitly considering the speed attribute of trajectory data, and to successfully achieve anomaly detection. The shape of object motion trajectory is modeled using Kernel Density Estimation (KDE), making use of both the angle attribute of the trajectory and the speed of the moving object. An unsupervised clustering algorithm, based on the Information Bottleneck (IB) method, is employed for trajectory learning to obtain an adaptive number of trajectory clusters through maximizing the Mutual Information (MI) between the clustering result and a feature set of the trajectory data. Furthermore, we propose to effectively enhance the performance of IB by taking into account the clustering quality in each iteration of the clustering procedure. The trajectories are determined as either abnormal (infrequently observed) or normal by a measure based on Shannon entropy. Extensive tests on real-world and synthetic data show that the proposed technique behaves very well and outperforms the state-of-the-art methods. View Full-Text
Keywords: trajectory shape analysis; trajectory clustering; anomaly detection; Kernel Density Estimation; Mutual Information; Shannon entropy trajectory shape analysis; trajectory clustering; anomaly detection; Kernel Density Estimation; Mutual Information; Shannon entropy
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Guo, Y.; Xu, Q.; Li, P.; Sbert, M.; Yang, Y. Trajectory Shape Analysis and Anomaly Detection Utilizing Information Theory Tools. Entropy 2017, 19, 323.

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