BMVC 2004, Kingston, 7th-9th Sept, 2004

Order Matters: A Distributed Sampling Method for Multi-Object Tracking
K. Smith and D. Gatica-Perez (IDIAP Research Institute, Switzerland)

Multi-Object tracking (MOT) is an important problem in a number of
vision applications. For particle filter (PF) tracking, as the number of objects
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, sequential
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 performance.
We propose a method to search the state space that fairly distributes
these impoverishment effects between the objects by defining a set of mixture
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.
(pdf article)