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An extended Kalman filter for mouse tracking.

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Title: An extended Kalman filter for mouse tracking.
Authors: Choi, Hongjun1, Kim, Mingi1 mgkim@korea.ac.kr, Lee, Onseok2 leeos@sch.ac.kr
Source: Medical & Biological Engineering & Computing. Nov2018, Vol. 56 Issue 11, p2109-2123. 15p. 2 Color Photographs, 1 Illustration, 1 Diagram, 6 Graphs.
Subjects: Animal tracks, Tracking & trailing, Animal behavior, Kalman filtering, Mice behavior
Abstract: Animal tracking is an important tool for observing behavior, which is useful in various research areas. Animal specimens can be tracked using dynamic models and observation models that require several types of data. Tracking mouse has several barriers due to the physical characteristics of the mouse, their unpredictable movement, and cluttered environments. Therefore, we propose a reliable method that uses a detection stage and a tracking stage to successfully track mouse. The detection stage detects the surface area of the mouse skin, and the tracking stage implements an extended Kalman filter to estimate the state variables of a nonlinear model. The changes in the overall shape of the mouse are tracked using an oval-shaped tracking model to estimate the parameters for the ellipse. An experiment is conducted to demonstrate the performance of the proposed tracking algorithm using six video images showing various types of movement, and the ground truth values for synthetic images are compared to the values generated by the tracking algorithm. A conventional manual tracking method is also applied to compare across eight experimenters. Furthermore, the effectiveness of the proposed tracking method is also demonstrated by applying the tracking algorithm with actual images of mouse. Graphical abstract. [ABSTRACT FROM AUTHOR]
Copyright of Medical & Biological Engineering & Computing 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.)
Database: Engineering Source
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  Data: An extended Kalman filter for mouse tracking.
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  Data: <searchLink fieldCode="AR" term="%22Choi%2C+Hongjun%22">Choi, Hongjun</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Kim%2C+Mingi%22">Kim, Mingi</searchLink><relatesTo>1</relatesTo><i> mgkim@korea.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Lee%2C+Onseok%22">Lee, Onseok</searchLink><relatesTo>2</relatesTo><i> leeos@sch.ac.kr</i>
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  Data: <searchLink fieldCode="JN" term="%22Medical+%26+Biological+Engineering+%26+Computing%22">Medical & Biological Engineering & Computing</searchLink>. Nov2018, Vol. 56 Issue 11, p2109-2123. 15p. 2 Color Photographs, 1 Illustration, 1 Diagram, 6 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Animal+tracks%22">Animal tracks</searchLink><br /><searchLink fieldCode="DE" term="%22Tracking+%26+trailing%22">Tracking & trailing</searchLink><br /><searchLink fieldCode="DE" term="%22Animal+behavior%22">Animal behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink><br /><searchLink fieldCode="DE" term="%22Mice+behavior%22">Mice behavior</searchLink>
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  Data: Animal tracking is an important tool for observing behavior, which is useful in various research areas. Animal specimens can be tracked using dynamic models and observation models that require several types of data. Tracking mouse has several barriers due to the physical characteristics of the mouse, their unpredictable movement, and cluttered environments. Therefore, we propose a reliable method that uses a detection stage and a tracking stage to successfully track mouse. The detection stage detects the surface area of the mouse skin, and the tracking stage implements an extended Kalman filter to estimate the state variables of a nonlinear model. The changes in the overall shape of the mouse are tracked using an oval-shaped tracking model to estimate the parameters for the ellipse. An experiment is conducted to demonstrate the performance of the proposed tracking algorithm using six video images showing various types of movement, and the ground truth values for synthetic images are compared to the values generated by the tracking algorithm. A conventional manual tracking method is also applied to compare across eight experimenters. Furthermore, the effectiveness of the proposed tracking method is also demonstrated by applying the tracking algorithm with actual images of mouse. Graphical abstract. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Medical & Biological Engineering & Computing 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/s11517-018-1805-4
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      – Code: eng
        Text: English
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        StartPage: 2109
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      – SubjectFull: Animal tracks
        Type: general
      – SubjectFull: Tracking & trailing
        Type: general
      – SubjectFull: Animal behavior
        Type: general
      – SubjectFull: Kalman filtering
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      – SubjectFull: Mice behavior
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      – TitleFull: An extended Kalman filter for mouse tracking.
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              M: 11
              Text: Nov2018
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              Y: 2018
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