کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6863938 | 1439530 | 2018 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A hybrid tracking framework based on kernel correlation filtering and particle filtering
ترجمه فارسی عنوان
چارچوب ردیابی ترکیبی مبتنی بر فیلتر کردن همبستگی هسته و فیلتر کردن ذرات
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کلمات کلیدی
هسته، فیلتر کردن همبستگی، برنامه نویسی انعطاف پذیر، ردیابی ویژوال فیلتر کردن ذرات،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Recently, the visual object tracking based on correlation filtering has achieved great success. However, there are still some problems need to be improved, such as the scale variation of the target, and so on. Particle filtering (PF) is another commonly used tracking technology. The drawback of PF is that a large number of particles is needed. In this paper, we propose a hybrid tracking framework based on a kernel correlation filtering model and a PF model to complement these two techniques. A local sparse coding is acted as the appearance model of the PF model. First, the kernel correlation filter model is used to obtain the preliminary position of the target. On the basis of the preliminary position of the target, the PF model is used to locate the target further and to capture the scale variation of the target. Finally, both qualitative and quantitative analyses on challenging benchmark with 100 sequences prove the effectiveness of our hybrid tracking framework.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 297, 5 July 2018, Pages 40-49
Journal: Neurocomputing - Volume 297, 5 July 2018, Pages 40-49
نویسندگان
Zhiqiang Zhao, Ping Feng, Jingjuan Guo, Caihong Yuan, Tianjiang Wang, Fang Liu, Zhijian Zhao, Zongmin Cui, Bin Wu,