Multi-Target Tracking with Dependent Likelihood Structures in Labeled Random Finite Set Filters

  title={Multi-Target Tracking with Dependent Likelihood Structures in Labeled Random Finite Set Filters},
  author={Lingji Chen},
  • Lingji Chen
  • Published in FUSION 8 August 2021
  • Computer Science
In multi-target tracking, a data association hypothesis assigns measurements to tracks, and the hypothesis likelihood (of the joint target-measurement associations) is used to compare among all hypotheses for truncation under a finite compute budget. It is often assumed however that an individual target-measurement association likelihood is independent of others, i.e., it remains the same in whichever hypothesis it belongs to. In the case of Track Oriented Multiple Hypothesis Tracking (TO-MHT… 

Figures and Tables from this paper



Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter

The present paper details efficient implementations of the δ-GLMB multi-target tracking filter and presents inexpensive look-ahead strategies to reduce the number of computations.

An algorithm for tracking multiple targets

  • D. Reid
  • Computer Science
    1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes
  • 1978
An algorithm for tracking multiple targets in a cluttered environment is developed. The algorithm is capable of initiating tracks, accounting for false or missing reports, and processing sets of

A merge/split algorithm for multitarget tracking using generalized labeled multi-Bernoulli filters

  • Lingji Chen
  • Computer Science
    Defense + Commercial Sensing
  • 2022
This work adopts a factored filtering density through the use of a novel Merge/Split algorithm, which allows us to determine when independence can be considered to hold approximately for a given tolerance, so that the "resolution" of tracking is adaptively chosen.

Multitarget Tracking

Multitarget tracking (MTT) refers to the problem of jointly estimating the number of targets and their states or trajectories from noisy sensor measurements. MTT has a long history spanning over 50

Multiobject Tracking for Generic Observation Model Using Labeled Random Finite Sets

This paper presents an exact Bayesian filtering solution for the multiobject tracking problem with the generic observation model, designed in the labeled random finite set framework, using the product styled representation of labeled multiobject densities.

Generalized Labeled Multi-Bernoulli Approximation of Multi-Object Densities

A tractable Generalized Labeled Multi-Bernoulli (GLMB) density is derived that matches the cardinality distribution and the first moment of the labeled multiobject distribution of interest and is demonstrated a tractable multiobject tracking algorithm for generic measurement models.

Joint Detection and Estimation of Multiple Objects From Image Observations

A multi-object filter suitable for image observations with low signal-to-noise ratio (SNR) is developed and a particle implementation of the multi- object filter is proposed and demonstrated via simulations.

Multiple Target Tracking Based on Sets of Trajectories

A solution of the multiple target tracking (MTT) problem based on sets of trajectories and the random finite set framework is proposed by considering multiobject density functions in which objects are trajectories.

Forty Years of Multiple Hypothesis Tracking - A Review of Key Developments

  • C. ChongS. MoriD. Reid
  • Computer Science
    2018 21st International Conference on Information Fusion (FUSION)
  • 2018
This paper reviews forty years of multiple hypothesis tracking's development, including the original measurement-oriented approach of Reid, track- oriented approach first formulated by Morefield, distributed processing, and recent graph-based approaches.

Extended Object Tracking: Introduction, Overview and Applications

An elaborate overview of current research in extended object tracking is provided, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.