tailieunhanh - Báo cáo hóa học: " Spatio-temporal Background Models for Outdoor Surveillance"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Spatio-temporal Background Models for Outdoor Surveillance | EURASIP Journal on Applied Signal Processing 2005 14 2281-2291 2005 Hindawi Publishing Corporation Spatio-temporal Background Models for Outdoor Surveillance Robert Pless Department of Computer Science and Engineering Washington University in St. Louis MO 63130 USA Email pless@ Received 2 January 2004 Revised 1 September 2004 Video surveillance in outdoor areas is hampered by consistent background motion which defeats systems that use motion to identify intruders. While algorithms exist for masking out regions with motion a better approach is to develop a statistical model of the typical dynamic video appearance. This allows the detection of potential intruders even in front of trees and grass waving in the wind waves across a lake or cars moving past. In this paper we present a general framework for the identification of anomalies in video and a comparison of statistical models that characterize the local video dynamics at each pixel neighborhood. A real-time implementation of these algorithms runs on an 800 MHz laptop and we present qualitative results in many application domains. Keywords and phrases anomaly detection dynamic backgrounds spatio-temporal image processing background subtraction real-time application. 1. INTRODUCTION Computer vision has had the most success in well-constrained environments. Well constrained environments allow the use of significant prior expectations explicit or controlled background models easily detectable features and effective closed-world assumptions. In many surveillance applications the environment cannot be explicitly controlled and may contain significant and irregular motion. However irregular the natural appearance of a scene as viewed by a static video camera is often highly constrained. Developing representations of these constraints models of the typical dynamic appearance of the scene will allow significant benefits to many vision algorithms. These models capture the dynamics of video captured from a .