Shape-based Human Detection for Threat Assessment.pdfVIP

D.J. Lee, P. Zhan, A. Thomas, R. Schoenberger, ”Shape-based Human Intrusion Detection”, SPIE International Symposium on Defense and Security, Visual Information Processing XIII, vol. 5438, p. 81-91, Orlando, Florida, USA, April 12-16, 2004. Shape-based Human Detection for Threat Assessment Dah-Jye Lee a , Pengcheng Zhan a , Aaron Thomas a , and Robert Schoenberger b a Department of Electrical and Computer Engineering, Brigham Young University, 459 CB, Provo, Utah 84602 b Agris-Schoen Vision Systems, Inc., 3320 Mill Springs Drive, Fairfax, VA 22031 ABSTRACT Detection of intrusions for early threat assessment requires the capability of distinguishing whether the intrusion is a human, an animal, or other objects. Most low-cost security systems use simple electronic motion detection sensors to monitor motion or the location of objects within the perimeter. Although cost effective, these systems suffer from high rates of false alarm, especially when monitoring open environments. Any moving objects including animals can falsely trigger the security system. Other security systems that utilize video equipment require human interpretation of the scene in order to make real-time threat assessment. Shape-based human detection technique has been developed for accurate early threat assessments for open and remote environment. Potential threats are isolated from the static background scene using differential motion analysis and contours of the intruding objects are extracted for shape analysis. Contour points are simplified by removing redundant points connecting short and straight line segments and preserving only those with shape significance. Contours are represented in tangent space for comparison with shapes stored in database. Power cepstrum technique has been developed to search for the best matched contour in database and to distinguish a human from other objects from different viewing angles and distances. Keywords: Threat assessm