کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4969684 | 1449978 | 2017 | 34 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Robust visual tracking via co-trained Kernelized correlation filters
ترجمه فارسی عنوان
ردیابی دقیق بصری از طریق فیلترهای همبستگی هسته ای هم آموزش یافته
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
Recent advances in visual tracking have witnessed the importance of discriminative classifiers tasked with distinguishing the target from the background. However, a single classifier may fail to cope with complex surrounding environment and large appearance variations of the target. Motivated by multi-view learning, we equip a basic framework to train a pool of discriminative classifiers jointly in a closed-form fashion in this paper. It poses an extra regularization term in ridge regression which interacts with other base models in the ensemble. Through a simple realization of this approach, we show co-trained kernelized correlation filters (COKCF) which consist of two KCF trackers, are able to outperform the KCF tracker by a larger margin and perform favorably against other state-of-the-art trackers on 63 benchmark video sequences.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 69, September 2017, Pages 82-93
Journal: Pattern Recognition - Volume 69, September 2017, Pages 82-93
نویسندگان
Le Zhang, Ponnuthurai Nagaratnam Suganthan,