Name
Title | ||
|---|---|---|
Switching hypothesized measurements: a dynamic model with applications to occlusion adaptive joint tracking |
Abstract | ||
|---|---|---|
This paper proposes a dynamic model supporting multimodal state space probability distributions and presents the application of the model in dealing with visual occlusions when tracking multiple objects jointly. For a set of hypotheses, multiple measurements are acquired at each time instant. The model switches among a set of hypothesized measurements during the propagation. Two computationally efficient filtering algorithms are derived for online joint tracking. Both the occlusion relationship and state of the objects are recursively estimated from the history of measurement data. The switching hypothesized measurements (SHM) model is generally applicable to describe various dynamic processes with multiple alternative measurement methods. |
Year | Venue | Keywords |
|---|---|---|
2003 | IJCAI | occlusion relationship,measurement data,dynamic model,various dynamic process,multimodal state space probability,multiple alternative measurement method,time instant,online joint tracking,multiple measurement,multiple object,adaptive joint tracking,state space,probability distribution |
Field | DocType | Citations |
Occlusion,Computer science,Filter (signal processing),Probability distribution,Artificial intelligence,State space,Machine learning,Recursion | Conference | 0 |
PageRank | References | Authors |
0.34 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Wang | 1 | 948 | 155.42 |
| Tele Tan | 2 | 173 | 28.33 |
| Kia-Fock Loe | 3 | 180 | 20.88 |