Bayesian tracking and parameter learning for non-linear multiple target tracking models
We propose a new Bayesian tracking and parameter learning algorithm for non-linear non-Gaussian multiple target tracking (MTT) models. We design a Markov chain Monte Carlo (MCMC) algorithm to sample from the posterior distribution of the target states, birth and death times, and association of observations to targets, which constitutes the solution to the tracking problem, as well as the model parameters. In the numerical section, we present performance comparisons with several competing techniques and demonstrate significant performance improvements in all cases.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Tracking multiple moving objects in images using Markov Chain Monte Carlo
A new Bayesian state and parameter learning algorithm for multiple target tracking (MTT) models with image observations is proposed. Specifically, a Markov chain Monte Carlo algorithm is designed to sample from the poste…
Image GenerationManeuvering, Multi-Target Tracking using Particle Filters
In this work, we develop tracking and estimation techniques relevant to underwater targets. Particularly, we explore particle filtering techniques for target tracking. It is a numerical approximation method for implement…
Joint Target Detection and Tracking in Multipath Environment: A Variational Bayesian Approach
We consider multitarget detection and tracking problem for a class of multipath detection system where one target may generate multiple measurements via multiple propagation paths, and the association relationship among …
Bayesian InferenceState EstimationTracking an Underwater Target with Unknown Measurement Noise Statistics Using Variational Bayesian Filters
This paper considers a bearings-only tracking problem using noisy measurements of unknown noise statistics from a passive sensor. It is assumed that the process and measurement noise follows the Gaussian distribution whe…
Deep Learning of Appearance Models for Online Object Tracking
This paper introduces a novel deep learning based approach for vision based single target tracking. We address this problem by proposing a network architecture which takes the input video frames and directly computes the…
Deep LearningObject Tracking