Name
Abstract | ||
|---|---|---|
We propose a switching hypothesized measurements (SHM) model supporting multimodal probability distributions and present the application of the model in handling potential variability in visual environments when tracking multiple objects jointly. For a set of occlusion hypotheses, a frame is measured once under each hypothesis, resulting in a set of measurements at each time instant. A computationally efficient SHM filter is derived for online joint region tracking. Both occlusion relationships and states of the objects are recursively estimated from the history of hypothesized measurements. The reference image is updated adaptively to deal with appearance changes of the objects. The SHM model is generally applicable to various dynamic processes with multiple alternative measurement methods. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/ICCV.2003.1238316 | ICCV |
Keywords | Field | DocType |
hidden feature removal,state-space methods,kalman filters,tracking filters,occlusion relationships andstates,alternative measurement method,occlusion hypotheses,history ofhypothesized measurement,joint region tracking,multiple object,presentsthe application ofthe model,image sequences,switching hypothesized measurements model,computationally efficient shm filter,recursive estimation,foronline joint region tracking,multimodal probability distribution,hypothesized measurements,occlusion hypothesis,appearance change,probability distribution,kalman filtering | Computer vision,Occlusion,Pattern recognition,Computer science,Reference image,Kalman filter,Probability distribution,Artificial intelligence,Recursion | Conference |
ISBN | Citations | PageRank |
0-7695-1950-4 | 4 | 0.68 |
References | Authors | |
17 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Wang | 1 | 948 | 155.42 |
| Tele Tan | 2 | 173 | 28.33 |
| Kia-Fock Loe | 3 | 180 | 20.88 |