Skip to main content

Advertisement

You are viewing the new BMC article page. Let us know what you think. Return to old version

Research Article | Open | Published:

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

Abstract

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.

Publisher note

To access the full article, please see PDF.

Author information

Correspondence to Yingping Huang.

Rights and permissions

Reprints and Permissions

About this article

Keywords

  • Image Sequence
  • Optic Flow
  • Object Tracking
  • Stereo Image
  • Binocular Vision