Learning Target Dynamics While Tracking Using Gaussian Processes
نویسندگان
چکیده
منابع مشابه
Learning Non-stationary System Dynamics Online Using Gaussian Processes
Gaussian processes are a powerful non-parametric framework for solving various regression problems. In this paper, we address the task of learning a Gaussian process model of non-stationary system dynamics in an online fashion. We propose an extension to previous models that can appropriately handle outdated training samples by decreasing their influence onto the predictive distribution. The re...
متن کاملTransfer Learning Based Visual Tracking with Gaussian Processes Regression
Modeling the target appearance is critical in many modern visual tracking algorithms. Many tracking-by-detection algorithms formulate the probability of target appearance as exponentially related to the confidence of a classifier output. By contrast, in this paper we directly analyze this probability using Gaussian Processes Regression (GPR), and introduce a latent variable to assist the tracki...
متن کاملAsymmetric kernel in Gaussian Processes for learning target variance
This work incorporates the multi-modality of the data distribution into a Gaussian Process regression model. We approach the problem from a discriminative perspective by learning, jointly over the training data, the target space variance in the neighborhood of a certain sample through metric learning. We start by using data centers rather than all training samples. Subsequently, each center sel...
متن کاملFocused Multi-task Learning Using Gaussian Processes
Given a learning task for a data set, learning it together with related tasks (data sets) can improve performance. Gaussian process models have been applied to such multi-task learning scenarios, based on joint priors for functions underlying the tasks. In previous Gaussian process approaches, all tasks have been assumed to be of equal importance, whereas in transfer learning the goal is asymme...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Aerospace and Electronic Systems
سال: 2020
ISSN: 0018-9251,1557-9603,2371-9877
DOI: 10.1109/taes.2019.2948699