کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4946712 | 1439415 | 2017 | 37 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Adaptive low-rank subspace learning with online optimization for robust visual tracking
ترجمه فارسی عنوان
یادگیری زیرمجموعه ای با کمترین رتبه بندی با بهینه سازی آنلاین برای ردیابی بصری قوی
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کلمات کلیدی
یادگیری زیر فضای کم بهینه سازی آنلاین، مجازات سازگار، ردیابی ویژوال
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
In recent years, sparse and low-rank models have been widely used to formulate appearance subspace for visual tracking. However, most existing methods only consider the sparsity or low-rankness of the coefficients, which is not sufficient enough for appearance subspace learning on complex video sequences. Moreover, as both the low-rank and the column sparse measures are tightly related to all the samples in the sequences, it is challenging to incrementally solve optimization problems with both nuclear norm and column sparse norm on sequentially obtained video data. To address above limitations, this paper develops a novel low-rank subspace learning with adaptive penalization (LSAP) framework for subspace based robust visual tracking. Different from previous work, which often simply decomposes observations as low-rank features and sparse errors, LSAP simultaneously learns the subspace basis, low-rank coefficients and column sparse errors to formulate appearance subspace. Within LSAP framework, we introduce a Hadamard production based regularization to incorporate rich generative/discriminative structure constraints to adaptively penalize the coefficients for subspace learning. It is shown that such adaptive penalization can significantly improve the robustness of LSAP on severely corrupted dataset. To utilize LSAP for online visual tracking, we also develop an efficient incremental optimization scheme for nuclear norm and column sparse norm minimizations. Experiments on 50 challenging video sequences demonstrate that our tracker outperforms other state-of-the-art methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 88, April 2017, Pages 90-104
Journal: Neural Networks - Volume 88, April 2017, Pages 90-104
نویسندگان
Risheng Liu, Di Wang, Yuzhuo Han, Xin Fan, Zhongxuan Luo,