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: Research Article Binocular Image Sequence Analysis: Integration of Stereo Disparity and Optic Flow for Improved Obstacle Detection and Tracking | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 843232 10 pages doi 2008 843232 Research Article Binocular Image Sequence Analysis Integration of Stereo Disparity and Optic Flow for Improved Obstacle Detection and Tracking Yingping Huang1 and Ken Young2 1 International Automotive Research Centre IARC Warwick Manufacturing Group University of Warwick Coventry CV4 7AL UK 2 International Manufacturing Research Centre Warwick Manufacturing Group University of Warwick Coventry CV4 7AL UK Correspondence should be addressed to Yingping Huang huang_y@ Received 28 August 2007 Revised 15 February 2008 Accepted 28 March 2008 Recommended by Ati Baskurt Binocular vision systems have been widely used for detecting obstacles in advanced driver assistant systems ADASs . These systems normally utilise disparity information extracted from left and right image pairs but ignore the optic flows able to be extracted from the two image sequences. In fact integration of these two methods may generate some distinct benefits. This paper proposes two algorithms for integrating stereovision and motion analysis for improving object detection and tracking. The basic idea is to fully make use of information extracted from stereo image sequence pairs captured from a stereovision rig. The first algorithm is to impose the optic flows as extra constraints for stereo matching. The second algorithm is to use a Kalman filter as a mixer to combine the distance measurement and the motion displacement measurement for object tracking. The experimental results demonstrate that the proposed methods are effective for improving the quality of stereo matching and three-dimensional object tracking. Copyright 2008 Y. Huang and K. Young. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work .
