Labeled Multi-Bernoulli Tracking for Industrial Mobile Platform Safety

TL;DR

This paper introduces a specialized labeled multi-Bernoulli filter for tracking human workers in industrial environments, utilizing shape and color cues to enhance safety monitoring.

Contribution

It develops a novel two-step Bayesian update for a labeled multi-Bernoulli filter tailored to industrial safety with shape and color likelihood functions.

Findings

Successfully tracks workers wearing luminous vests in simulations.

Demonstrates improved tracking accuracy with application-specific likelihoods.

Validates the approach for real-time safety monitoring in industrial settings.

Abstract

This paper presents a track-before-detect labeled multi-Bernoulli filter tailored for industrial mobile platform safety applications. We derive two application specific separable likelihood functions that capture the geometric shape and colour information of the human targets who are wearing a high visible vest. These likelihoods are then used in a labeled multi-Bernoulli filter with a novel two step Bayesian update. Preliminary simulation results show that the proposed solution can successfully track human workers wearing a luminous yellow colour vest in an industrial environment.