Lingjiang Kong

Labeled Multi-object Tracking Algorithms for Generic Observation Model

2016 · arXiv (Cornell University)

In this paper, we are devoted to the labeled multiobject tracking problem for generic observation model (GOM) in the framework of Finite set statistics. Firstly, we derive a product-labeled multi-object (P-LMO) filter following by the …

A fast implementation of distributed fusion with CPHD filter

2017

The paper addresses a fast implementation algorithm about distributed fusion with CPHD filter. An implementation method of distributed fusion based on maximum probability association (MPA), called MPA-DF, is presented. Though the performance of MPA-DF is …

Computationally Efficient Distributed Multi-Sensor Multi-Bernoulli Filter

2018

This paper proposes a computationally efficient distributed fusion algorithm with multi-Bernoulli (MB) random finite sets (RFSs) based on generalized Covariance Intersection (GCI). The GCI fusion with MB filter (GCI-MB) involves the computation of the generalized …

Multi-object Tracking for Generic Observation Model Using Labeled Random Finite Sets

2016 · arXiv (Cornell University)

This paper presents an exact Bayesian filtering solution for the multi-object tracking problem with the generic observation model. The proposed solution is designed in the labeled random finite set framework, using the product styled representation …

Data‐driven XGBoost‐based filter for target tracking

2019 · The Journal of Engineering

In recent years, the data‐driven approach has been introduced in the field of target tracking as a powerful tool developing the end‐to‐end mapping relationship between input features and outputs. Typically, in data‐driven methods, neural networks …