AN APPROACH TO AUTOMATIC DETECTION of SUSPICIOUS INDIVIDUALS IN A CROWD

Author

Satabdi Mukherjee, CUNY City CollegeFollow

Date of Award

2016

Document Type

Thesis

Department

Computer Science

First Advisor

Stephen Lucci

Second Advisor

Izidor Gertner

Keywords

k-means; clustering; geometric; verification; Indian-buffet; stochastic; suspicious; hierarchical tree

Abstract

This paper describes an approach to identify individuals with suspicious objects in a crowd. It is based on a well-known image retrieval problem as applied to mobile visual search. In many cases, the process of building 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 a congested clustering which can cause problems in grouping large real-time data. To overcome this problem we have applied the Indian Buffet stochastic process approach in this paper to the clustering problem. We present examples illustrating our method

Recommended Citation

Mukherjee, Satabdi, "AN APPROACH TO AUTOMATIC DETECTION of SUSPICIOUS INDIVIDUALS IN A CROWD" (2016). CUNY Academic Works.
https://academicworks.cuny.edu/cc_etds_theses/631