کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4946359 | 1439288 | 2016 | 23 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A multi-view model for visual tracking via correlation filters
ترجمه فارسی عنوان
یک مدل چندبعدی برای ردیابی دیداری از طریق فیلترهای همبستگی
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کلمات کلیدی
ردیابی شیء بصری، چندین نمایش فیلترهای همبستگی، پیگیری شدید
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Robustness and efficiency are the two main goals of existing trackers. Most robust trackers are implemented with combined features or models accompanied with a high computational cost. To achieve a robust and efficient tracking performance, we propose a multi-view correlation tracker to do tracking. On one hand, the robustness of the tracker is enhanced by the multi-view model, which fuses several features and selects the more discriminative features to do tracking. On the other hand, the correlation filter framework provides a fast training and efficient target locating. The multiple features are well fused on the model level of correlation filer, which are effective and efficient. In addition, we raise a simple but effective scale-variation detection mechanism, which strengthens the stability of scale variation tracking. We evaluate our tracker on online tracking benchmark (OTB) and two visual object tracking benchmarks (VOT2014, VOT2015). These three datasets contains more than 100 video sequences in total. On all the three datasets, the proposed approach achieves promising performance.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 113, 1 December 2016, Pages 88-99
Journal: Knowledge-Based Systems - Volume 113, 1 December 2016, Pages 88-99
نویسندگان
Xin Li, Qiao Liu, Zhenyu He, Hongpeng Wang, Chunkai Zhang, Wen-Sheng Chen,