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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wood, T.M. Yates, C.A. Wilkinson, D.A. Rosser, G. |
| Copyright Year | 1991 |
| Abstract | The probability hypothesis density (PHD) filter from the theory of random finite sets is a well-known method for multitarget tracking. We present the Gaussian mixture (GM) and improved sequential Monte Carlo implementations of the PHD filter for visual tracking. These implementations are shown to provide advantages over previous PHD filter implementations on visual data by removing complications such as clustering and data association and also having beneficial computational characteristics. The GM-PHD filter is deployed on microscopic visual data to extract trajectories of free-swimming bacteria in order to analyze their motion. Using this method, a significantly larger number of tracks are obtained than was previously possible. This permits calculation of reliable distributions for parameters of bacterial motion. The PHD filter output was tested by checking agreement with a careful manual analysis. A comparison between the PHD filter and alternative tracking methods was carried out using simulated data, demonstrating superior performance by the PHD filter in a range of realistic scenarios. |
| Sponsorship | IEEE Circuits and Systems Society |
| Starting Page | 702 |
| Ending Page | 713 |
| Page Count | 12 |
| File Size | 462785 |
| File Format | |
| ISSN | 10518215 |
| Volume Number | 22 |
| Issue Number | 5 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-05-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Target tracking Microorganisms Visualization Microscopy Noise measurement Mathematical model sequential Monte Carlo Bacterial motion multitarget tracking probability hypothesis density (PHD) filter random finite sets |
| Content Type | Text |
| Resource Type | Article |
| Subject | Electrical and Electronic Engineering Media Technology |
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