TY  - CONF
AU  - Naveen Onkarappa
AU  - Angel Sappa
A2  - CAIP
PY  - 2013//
TI  - Laplacian Derivative based Regularization for Optical Flow Estimation in Driving Scenario
T2  - LNCS
BT  - 15th International Conference on Computer Analysis of Images and Patterns
SP  - 483
EP  - 490
VL  - 8048
PB  - Springer Berlin Heidelberg
KW  - Optical flow
KW  - regularization
KW  - Driver Assistance Systems
KW  - Performance Evaluation
N2  - Existing state of the art optical flow approaches, which are evaluated on standard datasets such as Middlebury, not necessarily have a similar performance when evaluated on driving scenarios. This drop on performance is due to several challenges arising on real scenarios during driving. Towards this direction, in this paper, we propose a modification to the regularization term in a variational optical flow formulation, that notably improves the results, specially in driving scenarios. The proposed modification consists on using the Laplacian derivatives of flow components in the regularization term instead of gradients of flow components. We show the improvements in results on a standard real image sequences dataset (KITTI).
SN  - 0302-9743
SN  - 978-3-642-40245-6
UR  - http://dx.doi.org/10.1007/978-3-642-40246-3_60
N1  - ADAS; 600.055; 601.215
ID  - Naveen Onkarappa2013
ER  -