Abstracts for 2003 WOMOT Tutorials
June 21, 2003
|8:00 - 9:00 a.m.||
Target tracking consists of data association, filtering and fusion, and a number of algorithms are available to handle these issues in tracking small (or point) targets. In this presentation, we will review different algorithms, including the Kalman filter, Interacting Multiple Model (IMM) estimator, Probabilistic Data Association (PDA) algorithm, Multiple Hypothesis Tracking (MHT) algorithm and assignment techniques. Possible fusion architectures and performance measures for target tracking will be discussed as well. Simulation results on some realistic tracking scenarios will be presented.
|9:00 - 9:55 a.m.||
Ronald P. Mahler
|9:55 - 10:05 a.m.||Break|
|10:05 - 11:00 a.m||
Sequential Monte Carlo methods, or particle filters, provide a powerful Bayesian methodology for sequential inference in nonlinear non-Gaussian state-space systems. After an introduction to the approach, in an attempt to improve intuition as to how particle filters can be used to track multiple targets, two thrusts of current research will be described within the context of importance sampling. Firstly, the crucial role of the choice of importance distribution will be described in terms of changing the memory of a system that the samples must populate. Secondly, the use of analytic integration to reduce the Monte-Carlo variance will be explained through consideration of the resultant reduction in dimensionality of the state-space that the samples inhabit.
|11:00 - noon||
After reviewing the fundamental theory of multi-object particle filters, we'll describe the popular approaches to using particle filters in visual tracking problems. In particular, we will investigate the answers to the following two questions: (i) How can all appropriate data from a video sequence be incorporated into the inference process, so that particles hypothesizing different numbers of objects can be treated coherently? (ii) How can object births and deaths be modeled without either destroying the probabilistic validity of the particle filter or creating an excessive computational burden? In addition, we will discuss strategies for variance reduction that are particularly suited to visual tracking problems.