Abstract
Abstract. In most Photogrammetry and computer vision tasks, finding the corresponding points among images is required. Among many, the Lucas-Kanade optical flow estimation has been employed for tracking interest points as well as motion vector field estimation. This paper uses the IMU measurements to reconstruct the epipolar geometry and it integrates the epipolar geometry constraint with the brightness constancy assumption in the Lucas-Kanade method. The proposed method has been tested using the KITTI dataset. The results show the improvement in motion vector field estimation in comparison to the Lucas-Kanade optical flow estimation. The same approach has been used in the KLT tracker and it has been shown that using epipolar geometry constraint can improve the KLT tracker. It is recommended that the epipolar geometry constraint is used in advanced variational optical flow estimation methods.
Highlights
Without the loss of generality, the motion vector field computed by most optical flow estimation methods provides dense pixel correspondences between consecutive images, which is required in applications such as 3D recovery, activity analysis, imagebased rendering and modeling (Szeliski, n.d.)
The results of the proposed method is shown in Figure (4) and Lucas/Kanade optical flow estimation results are given for comparison purposes
We proposed a method to improve Lucas/Kanade optical flow using epipolar geometry constraint
Summary
Without the loss of generality, the motion vector field computed by most optical flow estimation methods provides dense pixel correspondences between consecutive images, which is required in applications such as 3D recovery, activity analysis, imagebased rendering and modeling (Szeliski, n.d.). It is a preprocessing step for the higher level missions like scene understanding. Optical flow estimation is based on the brightness (or color) constancy assumption It means that two corresponding pixels in consecutive images at times t and t + 1 should have the same brightness: I(x, y, t) = I(x + u, y + v, t + 1).
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