1. Non-Bayesian Track-Before-Detect Using Cauchy-Schwarz Divergence-Based Information Fusion
- Author
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Ruwan Tennakoon, Tharindu Rathnayake, Alireza Bab-Haidashar, Reza Hoseinnezhad, and Amirali K. Gostar
- Subjects
0301 basic medicine ,Computer science ,Bayesian probability ,020206 networking & telecommunications ,02 engineering and technology ,Tracking (particle physics) ,Poisson distribution ,Track-before-detect ,Image (mathematics) ,03 medical and health sciences ,symbols.namesake ,030104 developmental biology ,0202 electrical engineering, electronic engineering, information engineering ,symbols ,Divergence (statistics) ,Finite set ,Cauchy–Schwarz inequality ,Algorithm - Abstract
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.
- Published
- 2018
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