TY - GEN
T1 - Detection and segmentation of independently moving objects from dense scene flow
AU - Wedel, Andreas
AU - Meißner, Annemarie
AU - Rabe, Clemens
AU - Franke, Uwe
AU - Cremers, Daniel
PY - 2009
Y1 - 2009
N2 - We present an approach for identifying and segmenting independently moving objects from dense scene flow information, using a moving stereo camera system. The detection and segmentation is challenging due to camera movement and non-rigid object motion. The disparity, change in disparity, and the optical flow are estimated in the image domain and the three-dimensional motion is inferred from the binocular triangulation of the translation vector. Using error propagation and scene flow reliability measures, we assign dense motion likelihoods to every pixel of a reference frame. These likelihoods are then used for the segmentation of independently moving objects in the reference image. In our results we systematically demonstrate the improvement using reliability measures for the scene flow variables. Furthermore, we compare the binocular segmentation of independently moving objects with a monocular version, using solely the optical flow component of the scene flow.
AB - We present an approach for identifying and segmenting independently moving objects from dense scene flow information, using a moving stereo camera system. The detection and segmentation is challenging due to camera movement and non-rigid object motion. The disparity, change in disparity, and the optical flow are estimated in the image domain and the three-dimensional motion is inferred from the binocular triangulation of the translation vector. Using error propagation and scene flow reliability measures, we assign dense motion likelihoods to every pixel of a reference frame. These likelihoods are then used for the segmentation of independently moving objects in the reference image. In our results we systematically demonstrate the improvement using reliability measures for the scene flow variables. Furthermore, we compare the binocular segmentation of independently moving objects with a monocular version, using solely the optical flow component of the scene flow.
UR - http://www.scopus.com/inward/record.url?scp=70350605617&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-03641-5_2
DO - 10.1007/978-3-642-03641-5_2
M3 - Conference contribution
AN - SCOPUS:70350605617
SN - 3642036406
SN - 9783642036408
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 14
EP - 27
BT - Energy Minimization Methods in Computer Vision and Pattern Recognition - 7th International Conference, EMMCVPR 2009, Proceedings
T2 - 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2009
Y2 - 24 August 2009 through 27 August 2009
ER -