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Proceedings Paper

An approach to automatic detection of suspicious individuals in a crowd
Author(s): Stephen Lucci; Satabdi Mukherjee; Izidor Gertner
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Paper Abstract

This paper describes an approach to identify individuals with suspicious objects in a crowd. To accomplish this goal we define criteria for a suspicious individual we are searching for. The query image is declared to contain a suspicious individual if it satisfies these criteria. In our implementation we apply a well-known algorithm suite used in image retrieval, mobile visual search problems where the reference data base of images is stored in a hierarchical tree data structure. In many cases, the construction of such a hierarchical tree uses k-means clustering followed by geometric verification. However, the number of clusters is not known in advance, and sometimes it is randomly generated. This may lead to congested clustering which can cause problems in grouping large real-time data. To overcome this problem, in this work, we estimate the number of clusters using the Indian Buffet stochastic process. We present examples illustrating our method.

Paper Details

Date Published: 22 May 2015
PDF: 14 pages
Proc. SPIE 9476, Automatic Target Recognition XXV, 94760C (22 May 2015); doi: 10.1117/12.2182671
Show Author Affiliations
Stephen Lucci, The City College of New York (United States)
Satabdi Mukherjee, The City College of New York (United States)
Izidor Gertner, The City College of New York (United States)

Published in SPIE Proceedings Vol. 9476:
Automatic Target Recognition XXV
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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