Non-Bayesian Track-Before-Detect Using Cauchy-Schwarz Divergence-Based Information Fusion

In this paper we present a novel non-Bayesian filtering method for tracking multiple objects with a particular application in time-lapse cell microscopic video sequence. In our method the heat-map of the frame sequence is extracted and represented as a pseudo-probability hypothesis density of the image. The pseudo-probability hypothesis density is used as measurements and fused with a prior Poisson random finite set density. We employed Cauchy-Schwarz divergence for information fusion. The presented algorithm was tested on a publicly available cell microscopic video sequence.