A NOVEL MULTILINEAR PREDICTOR FOR FAST VISUAL TRACKING  

作　　者：Guo Weiwei Yu Wenxian 

机构地区：[1]ATR Laboratory,School of Electronic Science and Engineering,National University of Defense Technology,Changsha 410073,China [2]School of Electronic Information and Electrical Engineering,Shanghai Jiaotong University,Shanghai 200433,China

出　　处：《Journal of Electronics(China)》2012年第1期158-165,共8页电子科学学刊（英文版）

摘　　要：This letter presents a novel prediction scheme employed for fast visual tracking. The proposed multilinear predictor is formulated as a higher order tensor, instead of the existing vector representations. This predictor is based on emploing the Canonical/Parallel factors (CP) decomposition to decompose a tensor as a sum of rank one tensors. In that way, the proposed scheme efficiently retains the underlying structural information of the input data, while reduces at the same time the computational complexity by employing separable filter operations applied at different directions. The efficiency of the proposed scheme is demonstrated in the conducted experiments.This letter presents a novel prediction scheme employed for fast visual tracking. The proposed multilinear predictor is formulated as a higher order tensor, instead of the existing vector representations. This predictor is based on emploing the Canonical/Parallel factors （CP） decomposition to decompose a tensor as a sum of rank one tensors. In that way, the proposed scheme efficiently retains the underlying structural information of the input data, while reduces at the same time the compu- tational complexity by employing separable filter operations applied at different directions. The effi- ciency of the proposed scheme is demonstrated in the conducted experiments.

关 键 词：Regression TENSOR TRACKING Hough voting 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]