A NOVEL MULTILINEAR PREDICTOR FOR FAST VISUAL TRACKING |
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Affiliation: | (ATR Laboratory, School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China) *(School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai 200433, China) |
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Abstract: | 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. |
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Keywords: | Key words Regression Tensor Tracking Hough voting |
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