Name
Abstract | ||
|---|---|---|
Multi-Object tracking (MOT) is an important problem in a number of vision applications. For particle filter (PF) tracking, as the number of ob- jects tracked increases, the search space for random sampling explodes in dimension. Partitioned sampling (PS) solves this problem by partitioning the search space, then searching each partition sequentially. However, sequen- tial weighted resampling steps cause an impoverishment effect that increases with the number of objects. This effect depends on the specific order in which the partitions are explored, creating an erratic and undesirable perfor- mance. We propose a method to search the state space that fairly distributes these impoverishment effects between the objects by defining a set of mix- ture components and performing PS in each of these components using one of a small set of representative object orderings. Using synthetic and real data, we show that our method retains the overall performance and reduced computational cost of PS, while improving performance in scenes where the impoverishment effect is significant. |
Year | Venue | Keywords |
|---|---|---|
2004 | BMVC | vision,sampling methods,particle filter,object tracking,random sampling,state space,search space |
Field | DocType | Citations |
Computer vision,Computer science,Particle filter,Video tracking,Sampling (statistics),Artificial intelligence,Partition (number theory),Small set,State space,Resampling | Conference | 19 |
PageRank | References | Authors |
1.72 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kevin Smith | 1 | 2430 | 88.78 |
| Daniel Gatica-Perez | 2 | 4182 | 276.74 |