Security Applications Using Computer Vision

Security Applications Using Computer Vision

Sreela Sasi (Gannon University, USA)
Copyright: © 2013 |Pages: 18
DOI: 10.4018/978-1-4666-3994-2.ch055
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Abstract

Computer vision plays a significant role in a wide range of homeland security applications. The homeland security applications include: port security (cargo inspection), facility security (embassy, power plant, bank), and surveillance (military or civilian), et cetera. Video surveillance cameras are placed in offices, hospitals, banks, ports, parking lots, parks, stadiums, malls, train stations, airports, et cetera. The challenge is not for acquiring surveillance data from these video cameras, but for identifying what is valuable, what can be ignored, and what demands immediate attention. Computer vision systems attempt to construct meaningful and explicit descriptions of the environment or scene captured in an image. A few Computer Vision based security applications are presented here for securing building facility, railroad (Objects on railroad, and red signal detection), and roads.
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Introduction

Securing Building Facility

Homeland security functions focus on intelligence and warning, protecting critical infrastructure and domestic counterterrorism. Biometric is a reliable way to authenticate the identity of a living person based on the physiological or behavioral characteristics. Gait of a person is a non-invasive biometric that can be used for recognition at a greater distance without the knowledge or cooperation of the person being recognized. Body weight, limb length, habitual posture, bone structure, and age influence the gait of a person. It has applications in visual surveillance, aware-spaces, and intelligent human-computer interfaces. These factors give each person a distinctive gait, which can be used as a biometric. The non-linear characteristics associated with gait pose a major challenge for research in this area. In this research, three methods are devised and evaluated for performance for the recognition of static postures in gait by combining Hidden Markov model with Visual Hull technique by Gomatam A.M., & Sasi S. (2004), Stereovision with 3D Template Matching by Gomatam A.M., & Sasi S. (2004) and Isoluminance lines with 3D Template Matching (TM) by Gomatam, A. M. & Sasi. S. (2005). These methods were tested on silhouettes of different person that are extracted from Carnegie Melon University’s Motion of Body (MoBo) database (2004) and performances were compared.

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