New preceding vehicle tracking algorithm based on optimal unbiased finite memory filter

Abstract In recent years, visual object tracking technologies have been used to track preceding vehicles in advanced driver assistance systems (ADASs). The accurate positioning of preceding vehicles in camera images allows drivers to avoid collisions with the preceding vehicle. Tracking systems typically take advantage of state estimators, such as the Kalman filter (KF) and the particle filter (PF), in order to suppress noises in measurements. In particular, the KF is popular in visual object tracking, because of its computational efficiency. However, the visual tracker based on the KF, referred to as the Kalman tracker (KT), has the drawback that its performance can decrease due to modeling and computational errors. To overcome this drawback, we propose a novel visual tracker based on the optimal unbiased finite memory filter (OUFMF) in the formulation of a linear matrix inequality (LMI) and a linear matrix equality (LME). We call the proposed visual tracker the finite memory tracker (FMT), and it is applied to the preceding vehicle tracking. Through extensive experiments, we demonstrate the FMT's performance that is superior to that of the KT and other filter-based tracker.