Name
Abstract | ||
|---|---|---|
This paper provides a novel technique of efficiently and reliably tracking features in a sequence of images. The method we provide for tracking features is based on the Bayesian multiple hypothesis tracking (MHT) technique coupled with a multiple model filtering (MMF) algorithm. We show the results of our work comparing it with some of the existing single-model-based trackers using a variety of video sequences. Initially, we demonstrate the ability of the MHT–MMF tracker, and later in the paper we extend the MMF-based tracker to the interacting multiple model (IMM) tracker and show the superiority of the latter in handling motion transition of features efficiently. The primary purpose of this paper is to show how the IMM algorithm combined with an extension of the classical MHT framework can be used in a visual tracking scenario. The study shows that the method proposed can distinguish between different motions depicted in an image sequence with good tracking results. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1016/S0031-3203(00)00019-4 | Pattern Recognition |
Keywords | Field | DocType |
Feature tracking,Multiple model filtering,Multiple hypothesis tracking,Interacting multiple model,Motion model switching | Computer vision,BitTorrent tracker,Multiple hypothesis tracking,Pattern recognition,Tracking system,Filter (signal processing),Eye tracking,Artificial intelligence,Image sequence,Mathematics,Feature tracking,Bayesian probability | Journal |
Volume | Issue | ISSN |
34 | 3 | 0031-3203 |
Citations | PageRank | References |
10 | 0.90 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| prithiraj tissainayagam | 1 | 104 | 7.09 |
| David Suter | 2 | 2247 | 126.18 |