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People trajectory mining with statistical pattern recognition

Abstract: People social interaction analysis is a complex and interesting problem that can be faced from several points of view depending on the application context. In videosurveillance contexts many indicators of people habits and relations exist and, among these, people trajectories analysis can reveal many aspects of the way people behave in social environments. We propose a statistical framework for trajectories mining that analyzes, in an integrated solution, several aspects of the trajectories such as location, shape and speed properties. Three different models are proposed to deal with non-idealities of the selected features in conjunction with a robust inexact- matching similarity measure for comparing sequences with different lengths. Experimental results in a real scenario demonstrates the efficacy of the framework in clustering people trajectories with the purpose of analyze frequent behaviors in complex environments.


Calderara, Simone; Cucchiara, Rita "People trajectory mining with statistical pattern recognition" Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on, San Francisco, USA, pp. 1 -8 , June 13 2010, 2010 DOI: 10.1109/CVPRW.2010.5543158

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