TY - GEN

T1 - Optical Flow Cluster Filtering for ROI Coding

AU - Meuel, Holger

AU - Munderloh, Marco

AU - Reso, Matthias

AU - Ostermann, Jorn

PY - 2013

Y1 - 2013

N2 - Current Moving Object Detectors in airborne Region of Interest (ROI) coding systems for police surveillance applications used on-board of UAVs are often based on Global Motion Estimation (GME) techniques. Since in these scenarios the camera is moving, simple background removal approaches cannot be applied without a Global Motion Compensation (GMC). Common GMC algorithms assume the ground to be planar, allowing the pixels of the previous frame to be motion compensated into the current frame by applying a projective transformation. The difference image between the compensated frame and the current frame emphasis regions containing possible motion. Such moving object detectors are great in terms of run-time efficiency but are known to lack in terms of accuracy - especially for unstructured regions of moving objects - as well as the robustness against noise. Superpixel segmentation was recently proposed to overcome the issue of the imprecise region cuts given by the difference image. It provides a greatly improved true positive detection rate, but unintentionally also increases the area of false positives. This paper proposes the use of a mesh-based GME and GMC to detect the moving object regions wherein a cluster filter eliminates errors in the optical flow by assuming a smooth vector field as the global motion model. In doing so we improve the coding efficiency of the fully automatic ROI coding system by more than 24% for moving object areas conserving the detection benefits of the integration of superpixel segmentation.

AB - Current Moving Object Detectors in airborne Region of Interest (ROI) coding systems for police surveillance applications used on-board of UAVs are often based on Global Motion Estimation (GME) techniques. Since in these scenarios the camera is moving, simple background removal approaches cannot be applied without a Global Motion Compensation (GMC). Common GMC algorithms assume the ground to be planar, allowing the pixels of the previous frame to be motion compensated into the current frame by applying a projective transformation. The difference image between the compensated frame and the current frame emphasis regions containing possible motion. Such moving object detectors are great in terms of run-time efficiency but are known to lack in terms of accuracy - especially for unstructured regions of moving objects - as well as the robustness against noise. Superpixel segmentation was recently proposed to overcome the issue of the imprecise region cuts given by the difference image. It provides a greatly improved true positive detection rate, but unintentionally also increases the area of false positives. This paper proposes the use of a mesh-based GME and GMC to detect the moving object regions wherein a cluster filter eliminates errors in the optical flow by assuming a smooth vector field as the global motion model. In doing so we improve the coding efficiency of the fully automatic ROI coding system by more than 24% for moving object areas conserving the detection benefits of the integration of superpixel segmentation.

KW - Aerial surveillance

KW - Global motion compensation

KW - Low bit rate video coding

KW - Mesh

KW - Moving object detection

KW - Optical flow

KW - ROI Coding

KW - Superpixel

UR - http://www.scopus.com/inward/record.url?scp=84897678874&partnerID=8YFLogxK

U2 - 10.1109/PCS.2013.6737700

DO - 10.1109/PCS.2013.6737700

M3 - Conference contribution

AN - SCOPUS:84897678874

SN - 9781479902941

T3 - 2013 Picture Coding Symposium, PCS 2013 - Proceedings

SP - 129

EP - 132

BT - 2013 Picture Coding Symposium

PB - IEEE Computer Society

T2 - 2013 Picture Coding Symposium, PCS 2013

Y2 - 8 December 2013 through 11 December 2013

ER -