A linear algorithm for multi-target tracking in the context of possibility theory

TL;DR

This paper introduces a possibility theory-based framework for multi-target tracking that enhances flexibility and handles uncertainty better than traditional methods, demonstrated through simulations.

Contribution

It develops a novel possibility theory approach for multi-target tracking, including new variants of point process and intensity functions, and derives an analogue of the PHD filter.

Findings

Framework effectively models uncertainty in real data scenarios.

Allows observation-driven birth schemes and initial target number modeling.

Demonstrates promising results on simulated data.

Abstract

We present a modelling framework for multi-target tracking based on possibility theory and illustrate its ability to account for the general lack of knowledge that the target-tracking practitioner must deal with when working with real data. We also introduce and study variants of the notions of point process and intensity function, which lead to the derivation of an analogue of the probability hypothesis density (PHD) filter. The gains provided by the considered modelling framework in terms of flexibility lead to the loss of some of the abilities that the PHD filter possesses; in particular the estimation of the number of targets by integration of the intensity function. Yet, the proposed recursion displays a number of advantages such as facilitating the introduction of observation-driven birth schemes and the modelling the absence of information on the initial number of targets in the scene. The performance of the proposed approach is demonstrated on simulated data.