Consensus Labeled Random Finite Set Filtering for Distributed Multi-Object Tracking

Abstract

This paper addresses distributed multi-object tracking over a network of heterogeneous and geographically dispersed nodes with sensing, communication and processing capabilities. The main contribution is an approach to distributed multi-object estimation based on labeled Random Finite Sets (RFSs) and dynamic Bayesian inference, which enables the development of two novel consensus tracking filters, namely a Consensus Marginalized $\delta$-Generalized Labeled Multi-Bernoulli and Consensus Labeled Multi-Bernoulli tracking filter. The proposed algorithms provide fully distributed, scalable and computationally efficient solutions for multi-object tracking. Simulation experiments via Gaussian mixture implementations confirm the effectiveness of the proposed approach on challenging scenarios.

References in corpus (2)

• Generalized Labeled Multi-Bernoulli Approximation of Multi-Object Densities
• The Marginalized $\delta$-GLMB Filter

Cited by in corpus (11)

• Distributed Fusion with Multi-Bernoulli Filter based on Generalized Covariance Intersection
• Robust Distributed Fusion with Labeled Random Finite Sets
• Fusion of Probability Density Functions
• Fusion of labeled RFS densities with minimum information loss
• Distributed Multi-object Tracking under Limited Field of View Sensors
• Multi-Sensor Control for Multi-Object Bayes Filters
• Multi-Sensor Control for Multi-Target Tracking Using Cauchy-Schwarz Divergence
• Distributed Multi-Sensor Fusion Using Generalized Multi-Bernoulli Densities
• Multiobject fusion with minimum information loss
• Distributed multi-object tracking over sensor networks: a random finite set approach
• Sliding-Window Optimization on an Ambiguity-Clearness Graph for Multi-object Tracking