Computer Vision Tracking Using Particle Filters for 3D Position Estimation

This line of research seeks to increase knowledge of a tracked target using the particle filter, also known as Sequential Monte Carlo (SMC) methods. The target is tracked using vision based observations. These observations were simulated using both dual cameras and a single camera. If only a single camera tracks the target, depth cannot be determined directly and is considered an unobservable state. Filters can estimate this unobservable state using a dynamic model and data from the image. However the movement of the target is nonlinear which eliminated filters traditionally used to track motion such as the Kalman filter and its variants. The particle filter is an alternative that can track nonlinear motion, but was not feasible until recently due to its computational requirements. Simulations of nonlinear target movement, first in two dimensions, then three, evaluated the particle filter\u27s feasibility and performance. Subsequent simulations evaluated the particle filter\u27s ability to track a target using dual and single camera observations. Evaluation tests were devised to characterize the performance of each filter. Analysis metrics were produced to analyze the results of these tests. Linear and Kalman filters were also devised to serve as additional comparisons to the particle filter. Results for dual camera observations demonstrated the filter could track the target and determine unobservable states, however results for the single camera observations indicated the filter was problematic since it could not return accurate depth estimates and suffered from severe weight collapse