TY - JOUR
T1 - Spherical Formulation of Geometric Motion Segmentation Constraints in Fisheye Cameras
AU - Mariotti, Letizia
AU - Eising, Ciaran
N1 - Publisher Copyright:
© 2000-2011 IEEE.
PY - 2022/5/1
Y1 - 2022/5/1
N2 - We introduce a visual motion segmentation method employing spherical geometry for fisheye cameras and automated driving. Three commonly used geometric constraints in pin-hole imagery (the positive height, positive depth and epipolar constraints) are reformulated to spherical coordinates, making them invariant to specific camera configurations as long as the camera calibration is known. A fourth constraint, known as the anti-parallel constraint, is added to resolve motion-parallax ambiguity, to support the detection of moving objects undergoing parallel or near-parallel motion with respect to the host vehicle. A final constraint constraint is described, known as the spherical three-view constraint, is described though not employed in our proposed algorithm. Results are presented and analyzed that demonstrate that the proposal is an effective motion segmentation approach for direct employment on fisheye imagery.
AB - We introduce a visual motion segmentation method employing spherical geometry for fisheye cameras and automated driving. Three commonly used geometric constraints in pin-hole imagery (the positive height, positive depth and epipolar constraints) are reformulated to spherical coordinates, making them invariant to specific camera configurations as long as the camera calibration is known. A fourth constraint, known as the anti-parallel constraint, is added to resolve motion-parallax ambiguity, to support the detection of moving objects undergoing parallel or near-parallel motion with respect to the host vehicle. A final constraint constraint is described, known as the spherical three-view constraint, is described though not employed in our proposed algorithm. Results are presented and analyzed that demonstrate that the proposal is an effective motion segmentation approach for direct employment on fisheye imagery.
KW - automated driving
KW - computer vision
KW - fisheye
KW - Obstacle detection
UR - http://www.scopus.com/inward/record.url?scp=85098758917&partnerID=8YFLogxK
U2 - 10.1109/TITS.2020.3042759
DO - 10.1109/TITS.2020.3042759
M3 - Article
AN - SCOPUS:85098758917
SN - 1524-9050
VL - 23
SP - 4201
EP - 4211
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 5
ER -