  This paper proposes an online visual multi-object tracking (MOT) algorithm
that resolves object appearance-reappearance and occlusion. Our solution is
based on the labeled random finite set (LRFS) filtering approach, which in
principle, addresses disappearance, appearance, reappearance, and occlusion via
a single Bayesian recursion. However, in practice, existing numerical
approximations cause reappearing objects to be initialized as new tracks,
especially after long periods of being undetected. In occlusion handling, the
filter's efficacy is dictated by trade-offs between the sophistication of the
occlusion model and computational demand. Our contribution is a novel modeling
method that exploits object features to address reappearing objects whilst
maintaining a linear complexity in the number of detections. Moreover, to
improve the filter's occlusion handling, we propose a fuzzy detection model
that takes into consideration the overlapping areas between tracks and their
sizes. We also develop a fast version of the filter to further reduce the
computational time.
