AN APPROACH TO AUTOMATIC DETECTION of SUSPICIOUS INDIVIDUALS IN A CROWD

<p>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</p>

Name thesis:degree_name

Master of Science (M.S.)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Year dc:date.available

2016

Author dc:creator

• Mukherjee, Satabdi

Contributors dc:contributor

• Stephen Lucci
• Izidor Gertner

• k-means; clustering; geometric; verification; Indian-buffet; stochastic; suspicious; hierarchical tree
• Artificial Intelligence and Robotics
• Computer Sciences
• Information Security

Repository record dc:identifier

https://academicworks.cuny.edu/cc_etds_theses/631

OAI identifier oai:identifier

oai:academicworks.cuny.edu:cc_etds_theses-1632