MARCO SAN BIAGIO1, SAMUELE MARTELLI1, MARCO CROCCO1, MARCO CRISTANI1,2, VITTORIO MURINO1,2 1 Pattern Analysis & Computer Vision Istituto Italiano di Tecnologia Via Morego 30, 16163, Genova, Italy 2 Università degli Studi di Verona Dipartimento di Informatica Strada le Grazie 15, 37134, Verona, Italy [email protected] http://iit.it/en/research/departments/pattern-analysis-and-computer-vision.html

This paper presents a novel statistical information fusion method to integrate multiple-view sensor data in multi-object tracking applications. The proposed method overcomes the drawbacks of the commonly used Generalized Covariance Intersection method, which considers constant weights allocated for sensors. Our method is based on enhancing the Generalized Covariance Intersection with adaptive w...

Let HI (resp., HZ) be a real and separable Hilbert space with Borel a-field f 1 (resp., f 2), and let (HIXH Z ' f 1 x f 2) be the product measurable space generated by the measurable rectangles. This paper develops relations between probability measures on (HIXH Z ' f 1 x f 2), i.e., joint measures, and the projections of such measures on (HI' f 1) and (HZ' f 2). In particular, the class of all...

A predictor sort experiment is one in which experimental units are allocated on the basis of the values of a predictor variable that is correlated with the response. Standard ANOVA analyses of predictor sort experiments can lead to confidence intervals whose actual coverages are poor matches to nominal coverages. Correct coverages can be obtained by adjusting confidence interval lengths by appr...

Article history: Received: 11.9.2013 Received in revised form: 6.11.2013 Accepted: 26.11.2013 In a multi-sensor target tracking system, the correlation of the sensors is unknown, and the cross-covariance between the local sensors can not be calculated. To solve the problem, the multisensor covariance intersection fusion steady-state Kalman filter is proposed. The advantage of the proposed metho...

The covariance matrix $\boldsymbol{\Sigma}$ of non-linear clustering statistics that are measured in current and upcoming surveys is fundamental interest for comparing cosmological theory data a crucial ingredient the likelihood approximations underlying widely used parameter inference forecasting methods. extreme number simulations needed to estimate sufficient accuracy poses severe challenge....