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Dense structural learning for infrared object tracking at 200+ Frames per Second.

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Title: Dense structural learning for infrared object tracking at 200+ Frames per Second.
Authors: Yu, Xianguo1,2, Yu, Qifeng1,2 yuxianguo_chn@163.com, Shang, Yang1,2, Zhang, Hongliang1,2
Source: Pattern Recognition Letters. Dec2017, Vol. 100, p152-159. 8p.
Subjects: Structural learning theory, Tracking algorithms, Feature selection, Feature extraction, C++
Abstract: Infrared object tracking is a key technology in many surveillance applications. General visual tracking algorithms designed for color images can not handle infrared targets very well due to their relatively low resolutions and blurred edges. This paper presents a new tracking by detection method based on online structural learning. We show how to train the classifier efficiently with dense samples through Fourier techniques and careful implementation. Furthermore, we introduce an effective feature representation for infrared objects. Finally, we demonstrate the performance of the proposed tracker on public infrared sequences with top accuracy and robustness. Meanwhile, our single thread C++ implementation of the algorithm achieves an average tracking speed of 215 FPS on a modern cpu. [ABSTRACT FROM AUTHOR]
Copyright of Pattern Recognition Letters is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: <searchLink fieldCode="AR" term="%22Yu%2C+Xianguo%22">Yu, Xianguo</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Yu%2C+Qifeng%22">Yu, Qifeng</searchLink><relatesTo>1,2</relatesTo><i> yuxianguo_chn@163.com</i><br /><searchLink fieldCode="AR" term="%22Shang%2C+Yang%22">Shang, Yang</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Hongliang%22">Zhang, Hongliang</searchLink><relatesTo>1,2</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Pattern+Recognition+Letters%22">Pattern Recognition Letters</searchLink>. Dec2017, Vol. 100, p152-159. 8p.
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  Data: Infrared object tracking is a key technology in many surveillance applications. General visual tracking algorithms designed for color images can not handle infrared targets very well due to their relatively low resolutions and blurred edges. This paper presents a new tracking by detection method based on online structural learning. We show how to train the classifier efficiently with dense samples through Fourier techniques and careful implementation. Furthermore, we introduce an effective feature representation for infrared objects. Finally, we demonstrate the performance of the proposed tracker on public infrared sequences with top accuracy and robustness. Meanwhile, our single thread C++ implementation of the algorithm achieves an average tracking speed of 215 FPS on a modern cpu. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Pattern Recognition Letters is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.patrec.2017.10.026
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      – Code: eng
        Text: English
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        PageCount: 8
        StartPage: 152
    Subjects:
      – SubjectFull: Structural learning theory
        Type: general
      – SubjectFull: Tracking algorithms
        Type: general
      – SubjectFull: Feature selection
        Type: general
      – SubjectFull: Feature extraction
        Type: general
      – SubjectFull: C++
        Type: general
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      – TitleFull: Dense structural learning for infrared object tracking at 200+ Frames per Second.
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            NameFull: Yu, Xianguo
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            – D: 01
              M: 12
              Text: Dec2017
              Type: published
              Y: 2017
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              Value: 100
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