کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4969952 | 1449988 | 2016 | 25 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Extended compressed tracking via random projection based on MSERs and online LS-SVM learning
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
The compressed tracking algorithm (CT tracker) is a well-known visual tracking method that models a target object׳s appearance through sparse random projection. However, the tracking results are not stable and robust due to the randomness of random projection. To solve this problem, a more stable and robust approach is proposed for visual tracking based on maximally stable extremal regions (MSERs), sparse random projection and online least squares SVM classifier (LS-SVM) learning. To obtain a relatively stable appearance model, the stable connected components of an object based on MSERs in image feature space are extracted. With the fusion of MSERs and sparse random projection, we model adaptive object appearance to adapt the variation of appearance. Additionally, an online closed-form LS-SVM is employed to quickly and robustly predict the target object location in a tracking by detection framework. Experimental results on benchmark sequences show the stability and robustness of the proposed algorithm compared with the existing CT-based trackers and other state-of-the-art trackers.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 59, November 2016, Pages 245-254
Journal: Pattern Recognition - Volume 59, November 2016, Pages 245-254
نویسندگان
Yuefang Gao, Xin Shan, Zexi Hu, Dong Wang, Ya Li, Xuhong Tian,