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Convolutional Shallow Features for Performance Improvement of Histogram of Oriented Gradients in Visual Object Tracking
Joint Authors
Lee, Hansoo
Kim, Eun Kyeong
Kim, Sungshin
Wibowo, Suryo Adhi
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-12-26
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract 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.
American Psychological Association (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
Modern Language Association (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
American Medical Association (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
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1191343