This paper presents a stratified auto-calibration framework for typical large surveillance set-ups including non-overlapping cameras. The framework avoids the need of any calibration target and purely relies on visual information coming from walking people. Since in nonoverlapping scenarios there are no point correspondences across the cameras the standard techniques cannot be employed. We show...

Imaging systems consisting of multiple conventional cameras are of increasing interest for computer vision applications such as Structure from Motion (SfM) due to their large combined field of view and high composite image resolution. In this work we present a SfM framework for multi-camera systems w/o overlapping camera views that integrates on-line extrinsic camera calibration, local scene re...

The robustness of robot calibration with respect to sensor noise is sensitive to the manipulator poses used to collect measurement data. In this paper we propose an algorithm based on a constrained optimization method, which allows us to choose a set of measurement configurations. It works by selecting iteratively one pose after another inside the workspace. After a few steps, a set of configur...

We document the calibration of the local volatility in terms of local and implied instantaneous variances; we first explore the theoretical properties of the method for a particular class of volatilities. We confirm the theoretical results through a numerical procedure which uses a Gauss-Newton style approximation of the Hessian in the framework of a sequential quadratic programming (SQP) appro...

Visual and inertial sensors, in combination, are able to provide accurate motion estimates and are well-suited for use in many robot navigation tasks. However, correct data fusion, and hence overall performance, depends on careful calibration of the rigid body transform between the sensors. Obtaining this calibration information is typically difficult and time-consuming, and normally requires a...

[1] A new global optimization algorithm, dynamically dimensioned search (DDS), is introduced for automatic calibration of watershed simulation models. DDS is designed for calibration problems with many parameters, requires no algorithm parameter tuning, and automatically scales the search to find good solutions within the maximum number of user-specified function (or model) evaluations. As a re...

Model calibration is a crucial step in building a reliable microscopic traffic simulation application, because it serves as the additional check to ensure the model parameters accurately reflect the local driving environment, such that decisions made based on these results would not be misinformed decisions. Because of its stochastic nature and complexity, the calibration problem, usually formu...