Vision-based long-range target detection using coarse-to-fine particle filter

Abstract

In this study, we develop a coarse-to-fine particle filter algorithm for track-before-detect in order to track a subpixel-sized, low signal-to-noise ratio target in sensor data. The proposed algorithm enhances tracking performance in the presence of target motion uncertainty and it also maintains the computational load without increasing the number of particles. This coarse-to-fine particle filter, which is newly applied to track-before-detect, has two recursive stages: a coarse stage for extensive searches of the target's state space and a fine stage that narrows down the tracking results. During the coarse stage, particles are propagated with uniformly distributed noise to compensate for highly nonlinear target motion. The fine stage disturbs the particles filtered from the coarse stage using Gaussian distributed noise. Monte Carlo simulation results using artificial image sequences indicate improved performance with the proposed algorithm when uncertain large frame-to-frame pixel differences are caused by nonlinear target motions such as jittering effects. The algorithm is also applied to the real camera image frames to verify its detecting performance.