An extended Kalman filter for mouse tracking.
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...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 56; no. 11; pp. 2109 - 2124 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=132461112&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132461112 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Nov2018 vid: 56 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 132461112 132461112 NLM29777506 10.1007/s11517-018-1805-4 NLM29777506 132461112 ppf: 2109 ppct: 15 formats: fmt: @attributes: type: P tig: atl: An extended Kalman filter for mouse tracking. aug: au: Choi, Hongjun Kim, Mingi Lee, Onseok affil: 3D Information Processing Laboratory, Department of Electronics and Information Engineering, Korea University, 126-1 Anam-dong, Seongbuk-gu, 02841, Seoul, South Korea sug: subj: Movement Physiology Behavior, Animal Mice Algorithms Animals Skin Physiopathology Image Processing, Computer Assisted Methods ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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