Name
Abstract | ||
|---|---|---|
Video-based target tracking is a challenging task, because there always appears to be complex occlusion among the varying number of objects. Also, in practice, it is very common that the objects in. a scene move irregularly with abrupt turns, which results in an interesting heavy-tailed phenomenon. As simulation has to run exceptionally long enough to capture the effect of the distribution tail, it is arduous to simulate heavy-tailed distribution. In this paper, we propose a new view to target tracking from a heavy-tailed perspective, establishing a simple but novel Multivariate Laplace Filter (MLF) tracking model., which efficiently and accurately describes the heavy-tailed issue and dramatically surmounts it. Some experimental results show the good performance of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICPR.2008.4761002 | 19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6 |
Keywords | Field | DocType |
noise,heavy tail,tracking,particle filters,heavy tailed distribution,statistical distributions,histograms | Histogram,Computer vision,Pattern recognition,Laplace transform,Multivariate statistics,Computer science,Particle filter,Probability distribution,Artificial intelligence | Conference |
ISSN | Citations | PageRank |
1051-4651 | 1 | 0.35 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daojing Wang | 1 | 1 | 0.69 |
| Chao Zhang | 2 | 174 | 13.53 |
| Xuemin Zhao | 3 | 22 | 5.28 |