Multi-target tracking with time-varying clutter rate and detection profile: Application to time-lapse cell microscopy sequences
© 2015 IEEE. Quantitative analysis of the dynamics of tiny cellular and sub-cellular structures, known as particles, in time-lapse cell microscopy sequences requires the development of a reliable multi-target tracking method capable of tracking numerous similar targets in the presence of high levels...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/37997 |
| _version_ | 1848755200541589504 |
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| author | Rezatofighi, S. Gould, S. Vo, Ba-Ngu Vo, Ba Tuong Mele, K. Hartley, R. |
| author_facet | Rezatofighi, S. Gould, S. Vo, Ba-Ngu Vo, Ba Tuong Mele, K. Hartley, R. |
| author_sort | Rezatofighi, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2015 IEEE. Quantitative analysis of the dynamics of tiny cellular and sub-cellular structures, known as particles, in time-lapse cell microscopy sequences requires the development of a reliable multi-target tracking method capable of tracking numerous similar targets in the presence of high levels of noise, high target density, complex motion patterns and intricate interactions. In this paper, we propose a framework for tracking these structures based on the random finite set Bayesian filtering framework. We focus on challenging biological applications where image characteristics such as noise and background intensity change during the acquisition process. Under these conditions, detection methods usually fail to detect all particles and are often followed by missed detections and many spurious measurements with unknown and time-varying rates. To deal with this, we propose a bootstrap filter composed of an estimator and a tracker. The estimator adaptively estimates the required meta parameters for the tracker such as clutter rate and the detection probability of the targets, while the tracker estimates the state of the targets. Our results show that the proposed approach can outperform state-of-the-art particle trackers on both synthetic and real data in this regime. |
| first_indexed | 2025-11-14T08:52:32Z |
| format | Journal Article |
| id | curtin-20.500.11937-37997 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:52:32Z |
| publishDate | 2015 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-379972018-03-29T09:07:09Z Multi-target tracking with time-varying clutter rate and detection profile: Application to time-lapse cell microscopy sequences Rezatofighi, S. Gould, S. Vo, Ba-Ngu Vo, Ba Tuong Mele, K. Hartley, R. © 2015 IEEE. Quantitative analysis of the dynamics of tiny cellular and sub-cellular structures, known as particles, in time-lapse cell microscopy sequences requires the development of a reliable multi-target tracking method capable of tracking numerous similar targets in the presence of high levels of noise, high target density, complex motion patterns and intricate interactions. In this paper, we propose a framework for tracking these structures based on the random finite set Bayesian filtering framework. We focus on challenging biological applications where image characteristics such as noise and background intensity change during the acquisition process. Under these conditions, detection methods usually fail to detect all particles and are often followed by missed detections and many spurious measurements with unknown and time-varying rates. To deal with this, we propose a bootstrap filter composed of an estimator and a tracker. The estimator adaptively estimates the required meta parameters for the tracker such as clutter rate and the detection probability of the targets, while the tracker estimates the state of the targets. Our results show that the proposed approach can outperform state-of-the-art particle trackers on both synthetic and real data in this regime. 2015 Journal Article http://hdl.handle.net/20.500.11937/37997 10.1109/TMI.2015.2390647 Institute of Electrical and Electronics Engineers Inc. restricted |
| spellingShingle | Rezatofighi, S. Gould, S. Vo, Ba-Ngu Vo, Ba Tuong Mele, K. Hartley, R. Multi-target tracking with time-varying clutter rate and detection profile: Application to time-lapse cell microscopy sequences |
| title | Multi-target tracking with time-varying clutter rate and detection profile: Application to time-lapse cell microscopy sequences |
| title_full | Multi-target tracking with time-varying clutter rate and detection profile: Application to time-lapse cell microscopy sequences |
| title_fullStr | Multi-target tracking with time-varying clutter rate and detection profile: Application to time-lapse cell microscopy sequences |
| title_full_unstemmed | Multi-target tracking with time-varying clutter rate and detection profile: Application to time-lapse cell microscopy sequences |
| title_short | Multi-target tracking with time-varying clutter rate and detection profile: Application to time-lapse cell microscopy sequences |
| title_sort | multi-target tracking with time-varying clutter rate and detection profile: application to time-lapse cell microscopy sequences |
| url | http://hdl.handle.net/20.500.11937/37997 |