طرح ردیابی دو جانبه برای ردیابی شیء بصری
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طرح ردیابی دو جانبه برای ردیابی شیء بصری

عنوان فارسی مقاله: طرح ردیابی دو جانبه برای ردیابی شیء بصری بر اساس حداقل مربعات متعامد بازگشتی
عنوان انگلیسی مقاله: Bidirectional Tracking Scheme for Visual Object Tracking Based on Recursive Orthogonal Least Squares
مجله/کنفرانس: دسترسی – IEEE Access
رشته های تحصیلی مرتبط: مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: مهندسی الگوریتم و محاسبات
کلمات کلیدی فارسی: ردیابی شیء بصری، طرح ردیابی دو جانبه، حداقل مربعات متعامد بازگشتی، مکانیسم به روزرسانی مدل
کلمات کلیدی انگلیسی: Visual object tracking, bidirectional tracking scheme, recursive orthogonal least squares, model update mechanism
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1109/ACCESS.2019.2951056
دانشگاه: College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350002, China
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.641 در سال 2018
شاخص H_index: 56 در سال 2019
شاخص SJR: 0.609 در سال 2018
شناسه ISSN: 2169-3536
شاخص Quartile (چارک): Q2 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 15
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13972
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


I. Introduction


II. Relate Work


III. Framework of This Proposed Method


IV. Extraction of HOGV and Color Histogram Descriptor


V. ROLS Based Classifier


Authors


Figures


References

نمونه متن انگلیسی مقاله

Abstract


Visual object tracking in unconstrained environments is a challenging task in computer vision. How to design an efficient discriminative feature representation is one challenging issue. To improve the adaptability of the tracker to large object appearance changes, the observation model needs to be updated online. However, a bad model update using inaccurate training samples can lead to model drift problem. Therefore, how to design an efficient online observation model and a model update strategy are two other challenging issues. This paper proposes the concatenation of histogram of oriented gradients variant (HOGv) and color histogram as the feature representation to balance discriminative power and efficiency. The single-hidden-layer feedforward neural network (SFNN) is used as an observation model, and the recursive orthogonal least squares (ROLS) algorithm is used to update the model online. A bidirectional tracking scheme is designed to alleviate the model drift problem during online tracking. The proposed bidirectional tracking scheme consists of three modules: the forward tracking module, the backward tracking module and the integration module. The forward tracking module first finds all the candidate regions, and then, the backward tracking module calculates the respective confidence of each candidate region according to historical information. Finally, the integration module integrates both of the first two modules’ results to determine the final tracked object and the model update strategy for the current frame. Extensive evaluations of the existing tracking benchmarks have shown that the proposed tracking framework results in significant performance improvements compared with the base tracker, and it outperforms most of the state-of-the-art trackers.


Introduction


Visual object tracking, which is used to estimate the trajectory of a target specified in the initial frame, is a fundamental topic in computer vision [1], [2]. Visual object tracking has numerous applications, such as intelligent video surveillance, intelligent transportation, human-computer interactions and so on. Despite significant progress in recent decades, visual object tracking is still a challenging problem due to irregular changes in appearance that are caused by partial or full occlusion, cluttered backgrounds, fast motion, deformation and illumination changes. Feature representation is one of the important factors for visual object tracking. Numerous hand-crafted features have been utilized for visual object tracking, such as color name [3], histograms of oriented gradient (HOG) [4], local binary pattern (LBP) [5] and so on. These hand-crafted features have relatively high computational efficiency but have been demonstrated to be less effective on the complex scene. Recently, convolutional neural networks (CNNs), with strong capabilities to learn feature representations, have demonstrated state-of-the-art performance in various computer vision tasks [6]–[8]. However, in visual object tracking, it is difficult to straightforwardly adopt CNNs, since they require a large number of training samples, and there is only one labeled positive sample that is extracted from the initial frames. One possible way is to utilize CNNs that have been trained on other tasks with a large-scale training dataset.

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