tailieunhanh - Báo cáo hóa học: " Robust Background Subtraction with Foreground Validation for Urban Traffic Video"

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: Robust Background Subtraction with Foreground Validation for Urban Traffic Video | EURASIP Journal on Applied Signal Processing 2005 14 2330-2340 2005 Hindawi Publishing Corporation Robust Background Subtraction with Foreground Validation for Urban Traffic Video Sen-Ching S. Cheung Center for Applied Scientific Computing Lawrence Livermore National Laboratory 7000 East Avenue Livermore CA 94550 USA Department of Electrical and Computer Engineering University of Kentucky Lexington KY 40506-0503 USA Email cheung@ Chandrika Kamath Center for Applied Scientific Computing Lawrence Livermore National Laboratory 7000 East Avenue Livermore CA 94550 USA Email kamath2@ Received 15 January 2004 Revised 29 December 2004 Identifying moving objects in a video sequence is a fundamental and critical task in many computer-vision applications. Background subtraction techniques are commonly used to separate foreground moving objects from the background. Most background subtraction techniques assume a single rate of adaptation which is inadequate for complex scenes such as a traffic intersection where objects are moving at different and varying speeds. In this paper we propose a foreground validation algorithm that first builds a foreground mask using a slow-adapting Kalman filter and then validates individual foreground pixels by a simple moving object model built using both the foreground and background statistics as well as the frame difference. Ground-truth experiments with urban traffic sequences show that our proposed algorithm significantly improves upon results using only Kalman filter or frame-differencing and outperforms other techniques based on mixture of Gaussians median filter and approximated median filter. Keywords and phrases background subtraction foreground validation urban traffic video. 1. INTRODUCTION Identifying moving objects in a video sequence is a fundamental and critical task in video surveillance traffic monitoring and analysis human detection and tracking and gesture recognition in human-machine interface. A common .