کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4968927 | 1449846 | 2017 | 28 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Learning spatially regularized similarity for robust visual tracking
ترجمه فارسی عنوان
یادگیری شباهت های منظم برای ردیابی بصری قوی
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کلمات کلیدی
ردیابی ویژوال نمایندگی مستقیمی، یادگیری متریک، شباهت محلی، به طور مرتب
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
Matching visual appearances of the target object over consecutive frames is a critical step in visual tracking. The accuracy performance of a practical tracking system highly depends on the similarity metric used for visual matching. Recent attempts to integrate discriminative metric learned by sequential visual data (instead of a predefined metric) in visual tracking have demonstrated more robust and accurate results. However, a global similarity metric is often suboptimal for visual matching when the target object experiences large appearance variation or occlusion. To address this issue, we propose in this paper a spatially weighted similarity fusion (SWSF) method for robust visual tracking. In our SWSF, a part-based model is employed as the object representation, and the local similarity metric and spatially regularized weights are jointly learned in a coherent process, such that the total matching accuracy between visual target and candidates can be effectively enhanced. Empirically, we evaluate our proposed tracker on various challenging sequences against several state-of-the-art methods, and the results demonstrate that our method can achieve competitive or better tracking performance in various challenging tracking scenarios.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Image and Vision Computing - Volume 60, April 2017, Pages 134-141
Journal: Image and Vision Computing - Volume 60, April 2017, Pages 134-141
نویسندگان
Xiuzhuang Zhou, Qirun Huo, Yuanyuan Shang, Min Xu, Hui Ding,