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Proceedings Paper

Human posture classification for intelligent visual surveillance systems
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Paper Abstract

Intelligent surveillance systems (ISS) have gained a significant attention in recent years due to the nationwide security concerns. Some of the important applications of ISS include: homeland security, border monitoring, battlefield intelligence, and sensitive facility monitoring. The essential requirements of an ISS include: (1) multi-modality multi-sensor data and information fusion, (2) communication networking, (3) distributed data/information processing,(4) Automatic target recognition and tracking, (5) Scenario profiling from discrete correlated/uncorrelated events, (6) Context-based situation reasoning, and (7) Collaborative resource sharing and decision support systems. In this paper we have addressed the problem of humanposture classification in crowded urban terrain environments. Certain range of human postures can be attributed to different suspicious acts of intruders in a constrained environment. By proper time analysis of human trespassers' postures in an environment, it would be possible to identify and differentiate malicious intention of the trespassers from other normal human behaviors. Specifically in this paper, we have proposed an image processing-based approach for characterization of five different human postures including: standing, bending, crawling, carrying a heavy object, and holding a long object. Two approaches were introduced to address the problem: template-matching and Hamming Adaptive Neural Network (HANN) classifiers. The former approach performs human posture characterization via binary-profile projection and applies a correlation-based method for classification of human postures. The latter approach is based a HANN technique. For training of the neural, the posture-patterns are initially compressed, thresholded, and serialized. The binary posture-pattern arrays were then used for training of the HANN. The comparative performance evaluation of both approaches the same set of training and testing examples were used to measure their effectiveness in classifying of five classes of human posture patterns. This paper presents and discusses the results of this experimental work. Both approaches demonstrated very promising results.

Paper Details

Date Published: 15 April 2008
PDF: 12 pages
Proc. SPIE 6983, Defense and Security 2008: Special Sessions on Food Safety, Visual Analytics, Resource Restricted Embedded and Sensor Networks, and 3D Imaging and Display, 69830F (15 April 2008); doi: 10.1117/12.777762
Show Author Affiliations
Haroun Rababaah, Tennessee State Univ. (United States)
Amir Shirkhodaie, Tennessee State Univ. (United States)

Published in SPIE Proceedings Vol. 6983:
Defense and Security 2008: Special Sessions on Food Safety, Visual Analytics, Resource Restricted Embedded and Sensor Networks, and 3D Imaging and Display
Moon S. Kim; Kaunglin Chao; William J. Tolone; William Ribarsky; Sergey I. Balandin; Bahram Javidi; Shu-I Tu, Editor(s)

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