A visual object tracking benchmark for cell motility in time-lapse imaging

Date

2019-09-01

Author

Demir, H. Seckin
Cetin, A. Enis
Atalay, Rengül

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Automatic tracking of cells is a widely studied problem in various biomedical applications. Although there are numerous approaches for the video object tracking task in different contexts, the performance of these methods depends on many factors regarding the specific application they are used for. This paper presents a comparative study that specifically targets cell tracking problem and compares performance behavior of the recent algorithms. We propose a framework for the performance evaluation of the tracking algorithms and compare several state-of-the-art object tracking approaches on an extensive time-lapse inverted microscopy dataset. We report the quantitative evaluations of the algorithms based on success rate and precision performance metrics.

Subject Keywords

Tracking benchmark, Cell motility, Visual object tracking

URI

https://hdl.handle.net/11511/31192

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Graduate School of Informatics, Article