An extended Kalman filter for mouse tracking
- Authors
- Choi, Hongjun; Kim, Mingi; Lee, Onseok
- Issue Date
- Nov-2018
- Publisher
- SPRINGER HEIDELBERG
- Keywords
- Mouse tracking; Extended Kalman filter; Sequential Monte Carlo
- Citation
- MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, v.56, no.11, pp.2109 - 2123
- Indexed
- SCIE
SCOPUS
- Journal Title
- MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
- Volume
- 56
- Number
- 11
- Start Page
- 2109
- End Page
- 2123
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/71961
- DOI
- 10.1007/s11517-018-1805-4
- ISSN
- 0140-0118
- 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.
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Collections - College of Science and Technology > Department of Electronics and Information Engineering > 1. Journal Articles
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