Appearance Fusion Based Motion-aware Architecture for Moving Object Segmentation
Moving object segmentation aims to segment all moving objects in the current scene,and it is of critical significance for many computer vision applications.At present,many moving object segmentation methods use the motion information from 2D optical flow maps to segment moving objects,which have many defects.For moving objects moving in the epipolar plane or mov-ing objects whose 3D motion direction are consistent with the background,it is difficult to identify these objects by the 2D optical flow maps.Besides,incorrect 2D optical flow also effects the result of moving object segmentation.To solve the above problems,this paper proposes different motion costs to improve the performance of moving object segmentation.In order to detect moving objects with coplanar and collinear motion,this paper proposes a balanced reprojection cost and a multi-angle optical flow contrast cost,which measures the difference between the 2D optical flow of moving objects and that of the background.For ego-motion de-generacy,this paper designs a differential homography cost.To segment moving objects in complex scenes,this paper proposes an appearance fusion based motion-aware architecture.In this architecture,in order to effectively fuse appearance features and motion features of objects,the multi-modality co-attention gate is adapted to achieve better interaction between appearance and motion cues.Besides,to emphasize moving objects,this paper introduces a multi-level motion based attention module to suppress redun-dant and misleading information.Extensive experiments are conducted on the KITTI dataset,the JNU-UISEE dataset,the Kitti-MoSeg dataset and the Davis-2016 dataset,and the proposed method achieves excellent performance.
万方数据
维普
