Online learning and fusion of orientation appearance models for robust rigid object tracking
作者:
Highlights:
• Robust learning and fusing of orientation appearance models.
• Combination of image gradient orientations with the directions of surface normals.
• Use of a robust kernel based on the Euler representation of angles.
• Performing 2D plus 3D rigid object tracking, achieving robust performance.
摘要
•Robust learning and fusing of orientation appearance models.•Combination of image gradient orientations with the directions of surface normals.•Use of a robust kernel based on the Euler representation of angles.•Performing 2D plus 3D rigid object tracking, achieving robust performance.
论文关键词:Rigid object tracking,Fusion of orientation appearance models,Subspace learning,Online learning,Face analysis,RGB-D
论文评审过程:Received 5 June 2013, Revised 14 February 2014, Accepted 5 April 2014, Available online 20 May 2014.
论文官网地址:https://doi.org/10.1016/j.imavis.2014.04.017