Uncertainty-Aware Variational Inference for Target Tracking

Variational Inference for Visual Tracking

The likelihood models used in probabilistic visual tracking applications are often complex non-linear and/or nonGaussian functions, leading to analytically intractable inference. Solutions then require numerical approximation techniques, of which the particle filter is a popular choice. Particle filters, however, degrade in performance as the dimensionality of the state space increases and the ...

Variational Inference for Logical Inference

Functional Distributional Semantics is a framework that aims to learn, from text, semantic representations which can be interpreted in terms of truth. Here we make two contributions to this framework. The first is to show how a type of logical inference can be performed by evaluating conditional probabilities. The second is to make these calculations tractable by means of a variational approxim...

Multiple Target Tracking with Navigation Uncertainty

The goal of concurrent mapping and localization (CML) is for a mobile robot to build a map of an unknown environment while simultaneously using that map to navigate. CML can be considered as a problem of multiple target tracking (MTT) in the presence of navigation uncertainty. Although data association errors can have a catastrophic e ect on CML performance, previous approaches to CML, such as ...

Representation of uncertainty in spatial target tracking

Quadrotor UAV Guidence For Ground Moving Target Tracking

The studies in aerial vehicles modeling and control have been increased rapidly recently. In this paper , a coordination of two types of heterogeneous robots , namely unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) is considered. In this paper the UAV plays the role of a virtual leader for the UGVs. The system consists of a vision- based target detection algorithm that uses the ...