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Single online visual object tracking with enhanced tracking and detection learning.

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Title: Single online visual object tracking with enhanced tracking and detection learning.
Authors: Luo, Liping1, Zheng, Zhenxian1, Yi, Yang1,2,3
Source: Multimedia Tools & Applications. May2019, Vol. 78 Issue 9, p12333-12351. 19p.
Subjects: Tracking & trailing, Learning, Statistical correlation, Optical flow, Data
Abstract: Single online visual object tracking has been an active research topic for its wide application on various tasks. In this paper, a new framework and related approaches are proposed to solve this problem consisting of enhanced tracking and detection learning. In the enhanced tracking part, an appearance model based on correlation filter with deep CNN features and a dynamic model using improved pyramid optical flow method are employed. Two models cooperate together to depict object appearance and capture target trajectory, which also contribute to provide training samples for detection learning. In the detection learning part, a cascade classifier and P-N learning scheme are employed to reinitialize tracking when model drift occurs. Data experiments on several challenging benchmarks show that the presented method is comparable to the state-of-the-art. [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature 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.)
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  Data: Single online visual object tracking with enhanced tracking and detection learning.
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  Data: Single online visual object tracking has been an active research topic for its wide application on various tasks. In this paper, a new framework and related approaches are proposed to solve this problem consisting of enhanced tracking and detection learning. In the enhanced tracking part, an appearance model based on correlation filter with deep CNN features and a dynamic model using improved pyramid optical flow method are employed. Two models cooperate together to depict object appearance and capture target trajectory, which also contribute to provide training samples for detection learning. In the detection learning part, a cascade classifier and P-N learning scheme are employed to reinitialize tracking when model drift occurs. Data experiments on several challenging benchmarks show that the presented method is comparable to the state-of-the-art. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature 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: 10.1007/s11042-018-6787-6
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        Text: English
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        Type: general
      – SubjectFull: Learning
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      – SubjectFull: Statistical correlation
        Type: general
      – SubjectFull: Optical flow
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      – SubjectFull: Data
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      – TitleFull: Single online visual object tracking with enhanced tracking and detection learning.
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              Text: May2019
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