Name
Abstract | ||
|---|---|---|
Particle filter is a sequential Monte Carlo method for object tracking in a recursive Bayesian filtering framework. The efficiency and accuracy of the particle filter depends on two key factors: how many particles are used and how these particles are re-located. In this paper, we estimate the number of required particles using the Kullback-Leibler distance (KLD), which is called KLD-sampling, and we use a hybrid dynamic model to generate diversified particles, which suits object's agile motion. Besides, we employ the mean shift analysis as a local mode seeking mechanism to make each particle more "informative". We demonstrate the performance of the proposed algorithm tracking the ball in sports video clips. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICME.2007.4285053 | 2007 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-5 |
Keywords | Field | DocType |
tracking, sampling methods | Computer vision,Monte Carlo method,Computer science,Particle filter,Filter (signal processing),Video tracking,Artificial intelligence,Mean-shift,Motion estimation,Monte Carlo localization,Particle | Conference |
Citations | PageRank | References |
2 | 0.40 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Huang | 1 | 2 | 0.40 |
| Joan Llach | 2 | 99 | 10.01 |