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Gaussian Process for Activity Modeling and Anomaly Detection : Volume Ii-3/W5, Issue 1 (20/08/2015)

By Liao, W.

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Book Id: WPLBN0004013944
Format Type: PDF Article :
File Size: Pages 8
Reproduction Date: 2015

Title: Gaussian Process for Activity Modeling and Anomaly Detection : Volume Ii-3/W5, Issue 1 (20/08/2015)  
Author: Liao, W.
Volume: Vol. II-3/W5, Issue 1
Language: English
Subject: Science, Isprs, Annals
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Yang, M. Y., Rosenhahn, B., & Liao, W. (2015). Gaussian Process for Activity Modeling and Anomaly Detection : Volume Ii-3/W5, Issue 1 (20/08/2015). Retrieved from

Description: Institute for Information Processing, Leibniz University Hannover, Hannover, Germany. Complex activity modeling and identification of anomaly is one of the most interesting and desired capabilities for automated video behavior analysis. A number of different approaches have been proposed in the past to tackle this problem. There are two main challenges for activity modeling and anomaly detection: 1) most existing approaches require sufficient data and supervision for learning; 2) the most interesting abnormal activities arise rarely and are ambiguous among typical activities, i.e. hard to be precisely defined. In this paper, we propose a novel approach to model complex activities and detect anomalies by using non-parametric Gaussian Process (GP) models in a crowded and complicated traffic scene. In comparison with parametric models such as HMM, GP models are nonparametric and have their advantages. Our GP models exploit implicit spatial-temporal dependence among local activity patterns. The learned GP regression models give a probabilistic prediction of regional activities at next time interval based on observations at present. An anomaly will be detected by comparing the actual observations with the prediction at real time. We verify the effectiveness and robustness of the proposed model on the QMUL Junction Dataset. Furthermore, we provide a publicly available manually labeled ground truth of this data set.



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