Abstract
We present a statistical approach to object tracking which allows for paths to merge together or split apart. Paths are also allowed to be born, die, and go undetected for several frames. The splitting and merging of paths is a novel addition for a statistically-based tracking algorithm. This addition is essential for storm tracking, which is the motivation for this work. The utility of this tracker extends well beyond the tracking of storms however. It can be valuable in other tracking applications that have splitting or merging, such as vortices, radar/sonar signals, or groups of people. The method assumes that the location of an object behaves like a Gaussian Process when it is observable. Objects are required to be born, die, split, or merge according to a Markov State Model. Path correspondence is achieved by an algorithm that finds the paths that maximize the likelihood of the assumed model.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1700-1704 |
| Number of pages | 5 |
| Journal | Conference Record - Asilomar Conference on Signals, Systems and Computers |
| Volume | 2 |
| State | Published - 2004 |
| Event | Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States Duration: Nov 7 2004 → Nov 10 2004 |
ASJC Scopus subject areas
- Signal Processing
- Computer Networks and Communications