Convolutional Shallow Features for Performance Improvement of Histogram of Oriented Gradients in Visual Object Tracking
المؤلفون المشاركون
Lee, Hansoo
Kim, Eun Kyeong
Kim, Sungshin
Wibowo, Suryo Adhi
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-12-26
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Histogram of oriented gradients (HOG) is a feature descriptor typically used for object detection.
For object tracking, this feature has certain drawbacks when the target object is influenced by a change in motion or size.
In this paper, the use of convolutional shallow features is proposed to improve the performance of HOG feature-based object tracking.
Because the proposed method works based on a correlation filter, the response maps for each feature are summed in order to obtain the final response map.
The location of the target object is then predicted based on the maximum value of the optimized final response map.
Further, a model update is used to overcome the change in appearance of the target object during tracking.
A performance evaluation of the proposed method is obtained by using Visual Object Tracking 2015 (VOT2015) benchmark dataset and its protocols.
The results are then provided based on their accuracy-robustness (AR) rank.
Furthermore, through a comparison with several state-of-the-art tracking algorithms, the proposed method was shown to achieve the highest rank in terms of accuracy and a third rank for robustness.
In addition, the proposed method significantly improves the robustness of HOG-based features.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wibowo, Suryo Adhi& Lee, Hansoo& Kim, Eun Kyeong& Kim, Sungshin. 2017. Convolutional Shallow Features for Performance Improvement of Histogram of Oriented Gradients in Visual Object Tracking. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1191343
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wibowo, Suryo Adhi…[et al.]. Convolutional Shallow Features for Performance Improvement of Histogram of Oriented Gradients in Visual Object Tracking. Mathematical Problems in Engineering No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1191343
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wibowo, Suryo Adhi& Lee, Hansoo& Kim, Eun Kyeong& Kim, Sungshin. Convolutional Shallow Features for Performance Improvement of Histogram of Oriented Gradients in Visual Object Tracking. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1191343
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1191343
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر