Binocular Image Sequence Analysis: Integration of Stereo Disparity and Optic Flow for Improved Obstacle Detection and Tracking

Open Access
Research Article


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.


Image Sequence Optic Flow Object Tracking Stereo Image Binocular Vision 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Y. Huang and K. Young. 2008

This article is published under license to BioMed Central Ltd. 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 is properly cited.

Authors and Affiliations

  1. 1.International Automotive Research Centre (IARC), Warwick Manufacturing GroupUniversity of WarwickCoventryUK
  2. 2.International Manufacturing Research Centre, Warwick Manufacturing GroupUniversity of WarwickCoventryUK

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