An approach to automatic detection of suspicious individuals in a crowd

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.