Binocular image sequence analysis : integration of stereo disparity and optic flow for improved obstacle detection and tracking
Huang, Yingping and Young, Ken. (2008) Binocular image sequence analysis : integration of stereo disparity and optic flow for improved obstacle detection and tracking. Eurasip Journal on Advances in Signal Processing, Vol.2008 (No.843232). ISSN 1687-6172
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Official URL: http://dx.doi.org/10.1155/2008/843232
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.
|Item Type:||Journal Article|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software|
|Divisions:||Faculty of Science > WMG (Formerly the Warwick Manufacturing Group)|
|Library of Congress Subject Headings (LCSH):||Computer vision, Binocular vision, Automobile driving -- Computer programs|
|Journal or Publication Title:||Eurasip Journal on Advances in Signal Processing|
|Official Date:||28 March 2008|
|Number of Pages:||10|
|Access rights to Published version:||Open Access|
 Y.Huang, S. Fu, and C. Thompson, “Stereovision-based object
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