Difference between revisions of "Tutorials" - 19th International Conference on Information Fusion

Revision as of 11:09, 23 February 2016

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List of Tutorials of FUSION 2016

T1 Bayesian Multiple Target Tracking

Presenter: Lawrence D. Stone, Roy L. Streit

T2 Bayesian Networks and Trust Fusion with Subjective Logic

Presenter: Audun Jøsang

T3 Multisensor-Multitarget Tracker/Fusion Engine Development and Performance Evaluation for Realistic Scenarios

Presenter: T. Kirubarajan

T4 An Introduction to Track-to-Track Fusion and the Distributed Kalman Filter

Presenter: Felix Govaers

T5 A Finite-Set Statistics Prime

Presenter: Ronald Mahler

T6 Information Quality in Information Fusion and Decision Making

Presenter: Galina Rogova

T7 Multitarget Tracking and Multisensor Information Fusion

Presenter: Yaakov Bar-Shalom

T8 Overview of High-Level Information Fusion Theory, Models, and Representations

Presenter: Erik Blasch

T9 Quantum Physics Methods For Nonlinear Filtering

Presenter: Bhashyam Balaji and Fred Daum

T10 Basic Concepts in Multiobject Estimation

Presenter: Daniel Clark, Emmanuel D. Delande, and Jérémie Houssineau

T11 System-of-Systems Concepts, Opportunities and Issues for Information Fusion

Presenter: Alan Steinberg

T12 Implementations of Random-Finite-Set-Based Multi-Target Filters

Presenter: Ba-Ngu Vo and Ba-Tuong Vo

T13 Tracking and Sensor Data Fusion – Methodological Framework and Selected Applications

Presenter: Wolfgang Koch

T14 Multistatic Exploration – Introduction to Modern Patutoialive Radar and Multistatic Tracking & Data Fusion

Presenter: Wolfgang Koch

T15 Big Data Fusion and Analytics

Presenter: Subrata Das

T16 Object Tracking, Sensor Fusion and Situational Awareness for Assisted- and Self-Driving Vehicles: Problems, Solutions and Directions

Presenter: T. Kirubarajan

T17 Emerging Quantum Technologies for Fusion

Presenter: Bhashyam Balaji

T18 Maneuvering Target Tracking: Overview and Nonlinear Filtering Methods

Presenter: X. Rong Li and Vesselin P. Jilkov

T19 Integration of Information to Identify Objects in Big Data

Presenter: Grace S. Shieh

T20 Extended Object Tracking: Theory and Applications

Presenter: Karl Granström, Stephan Reuter, and Marcus Baum

T21 Probabilistic Situation Atutoialetutoialment for Abnormal Interaction Detection

Presenter: Carlo Regazzoni and Lucio Marcenaro

T22 Multitarget Tracking and Sensor Calibration in Centralized and Distributed Networks

Presenter: Murat Uney, Simon Julier, and Daniel Clark

T23 Information Fusion in Resource-Limited Camera Networks

Presenter: Andrea Cavallaro and Juan C. SanMiguel

T24 Introduction to Bayesian Filtering and Smoothing

Presenter: Simo Särkkä

T25 Sensor Fusion for Intelligent Vehicles

Presenter: Bharanidhar Duraisamy, Ting Yuan, Tilo Schwarz, and Martin Fritzsche

T26 Multisensor Data Fusion in Wireless Sensor and Actuator Networks

Presenter: Claudio Miceli de Farias

@@ Line 32: / Line 32: @@
 * [[Tutorials#tutorial8| T8 Overview of High-Level Information Fusion Theory, Models, and Representations]]
 * [[Tutorials#tutorial9| T9 Quantum Physics Methods For Nonlinear Filtering]]
-* [[Tutorials#tutorial10| T10 Basic concepts in multiobject estimation]]
+* [[Tutorials#tutorial10| T10 Basic Concepts in Multiobject Estimation]]
 * [[Tutorials#tutorial11| T11 System-of-Systems Concepts, Opportunities and Issues for Information Fusion]]
-* [[Tutorials#tutorial12| T12 Implementations of random-finite-set-based multi-target filters]]
+* [[Tutorials#tutorial12| T12 Implementations of Random-Finite-Set-Based Multi-Target Filters]]
 * [[Tutorials#tutorial13| T13 Tracking and Sensor Data Fusion – Methodological Framework and Selected Applications]]
 * [[Tutorials#tutorial14| T14 Multistatic Exploration – Introduction to Modern Patutoialive Radar and Multistatic Tracking & Data Fusion]]
 * [[Tutorials#tutorial15| T15 Big Data Fusion and Analytics]]
-* [[Tutorials#tutorial16| T16 Object tracking, sensor fusion and situational awareness for assisted- and self-driving vehicles: Problems, solutions and directions]]
+* [[Tutorials#tutorial16| T16 Object Tracking, Sensor Fusion and Situational Awareness for Assisted- and Self-Driving Vehicles: Problems, Solutions and Directions]]
 * [[Tutorials#tutorial17| T17 Emerging Quantum Technologies for Fusion]]
 * [[Tutorials#tutorial18| T18 Maneuvering Target Tracking: Overview and Nonlinear Filtering Methods]]
 * [[Tutorials#tutorial19| T19 Integration of Information to Indentify Objects in Big Data]]
 * [[Tutorials#tutorial20| T20 Extended Object Tracking: Theory and Applications]]
-* [[Tutorials#tutorial21| T21 Probabilistic situation atutoialetutoialment for abnormal interaction detection]]
+* [[Tutorials#tutorial21| T21 Probabilistic Situation Atutoialetutoialment for Abnormal Interaction Detection]]
-* [[Tutorials#tutorial22| T22 Multitarget tracking and sensor calibration in centralized and distributed networks]]
+* [[Tutorials#tutorial22| T22 Multitarget Tracking and Sensor Calibration in Centralized and Distributed Networks]]
-* [[Tutorials#tutorial23| T23 Information fusion in resource-limited camera networks]]
+* [[Tutorials#tutorial23| T23 Information Fusion in Resource-Limited Camera Networks]]
 * [[Tutorials#tutorial24| T24 Introduction to Bayesian Filtering and Smoothing]]
 * [[Tutorials#tutorial25| T25 Sensor Fusion for Intelligent Vehicles]]
@@ Line 240: / Line 240: @@
 {| id="mp-upper" style="width: 80%; margin:4px 0 0 0; background:none; border-spacing: 0px;"
 <div id="tutorial10"></div>
-<!--        T10 Basic concepts in multiobject estimation        -->
+<!--        T10 Basic Concepts in Multiobject Estimation        -->
 | class="MainPageBG" style="width:100%; border:1px solid #d6bdde; background:#f7eff7; vertical-align:top; color:#000;" |
 {| id="mp-left" style="width:100%; vertical-align:top; background:#e7f7e76;"
-| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:4px; background:#e7deef; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #d6bdde; text-align:left; color:#000; padding:0.2em 0.4em;">T10 Basic concepts in multiobject estimation</h2>
+| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:4px; background:#e7deef; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #d6bdde; text-align:left; color:#000; padding:0.2em 0.4em;">T10 Basic Concepts in Multiobject Estimation</h2>
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 <div id="tutorial12"></div>
-<!--        T12 Implementations of random-finite-set-based multi-target filters        -->
+<!--        T12 Implementations of Random-Finite-Set-Based Multi-Target Filters        -->
 | class="MainPageBG" style="width:100%; border:1px solid #a3babf; background:#f5fdff; vertical-align:top; color:#000;" |
 {| id="mp-left" style="width:100%; vertical-align:top; background:#e7f7e76;"
-| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#ceecf2; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #a3babf; text-align:left; color:#000; padding:0.2em 0.4em;">T12 Implementations of random-finite-set-based multi-target filters</h2>
+| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#ceecf2; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #a3babf; text-align:left; color:#000; padding:0.2em 0.4em;">T12 Implementations of Random-Finite-Set-Based Multi-Target Filters</h2>
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 {| id="mp-upper" style="width: 80%; margin:4px 0 0 0; background:none; border-spacing: 0px;"
 <div id="tutorial16">
-<!--        T16 Object tracking, sensor fusion and situational awareness for assisted- and self-driving vehicles: Problems, solutions and directions      -->
+<!--        T16 Object Tracking, Sensor Fusion and Situational Awareness for Assisted- and Self-Driving Vehicles: Problems, Solutions and Directions      -->
 | class="MainPageBG" style="width:100%; border:1px solid #f36766; background:#f9d6c9; vertical-align:top; color:#000;" |
 {| id="mp-left" style="width:100%; vertical-align:top; background:#f9d6c9;"
-| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#f5baa3; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #f36766; text-align:left; color:#000; padding:0.2em 0.4em;">T16 Object tracking, sensor fusion and situational awareness for assisted- and self-driving vehicles: Problems, solutions and directions</h2>
+| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#f5baa3; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #f36766; text-align:left; color:#000; padding:0.2em 0.4em;">T16 Object Tracking, Sensor Fusion and Situational Awareness for Assisted- and Self-Driving Vehicles: Problems, Solutions and Directions</h2>
 |-
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@@ Line 462: / Line 462: @@
 {| id="mp-upper" style="width: 80%; margin:4px 0 0 0; background:none; border-spacing: 0px;"
 <div id="tutorial21">
-<!--        T21 Probabilistic situation assessment for abnormal interaction detection      -->
+<!--        T21 Probabilistic Situation Atutoialetutoialment for Abnormal Interaction Detection      -->
 | class="MainPageBG" style="width:100%; border:1px solid #f36766; background:#f9d6c9; vertical-align:top; color:#000;" |
 {| id="mp-left" style="width:100%; vertical-align:top; background:#f9d6c9;"
-| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#f5baa3; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #f36766; text-align:left; color:#000; padding:0.2em 0.4em;">T21 Probabilistic situation assessment for abnormal interaction detection</h2>
+| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#f5baa3; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #f36766; text-align:left; color:#000; padding:0.2em 0.4em;">T21 Probabilistic Situation Atutoialetutoialment for Abnormal Interaction Detection</h2>
 |-
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 {| id="mp-upper" style="width: 80%; margin:4px 0 0 0; background:none; border-spacing: 0px;"
 <div id="tutorial22"></div>
-<!--        T22 Multitarget tracking and sensor calibration in centralized and distributed networks       -->
+<!--        T22 Multitarget Tracking and Sensor Calibration in Centralized and Distributed Networks       -->
 | class="MainPageBG" style="width:100%; border:1px solid #a3babf; background:#f5fdff; vertical-align:top; color:#000;" |
 {| id="mp-left" style="width:100%; vertical-align:top; background:#e7f7e76;"
-| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#ceecf2; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #a3babf; text-align:left; color:#000; padding:0.2em 0.4em;">T22 Multitarget tracking and sensor calibration in centralized and distributed networks</h2>
+| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#ceecf2; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #a3babf; text-align:left; color:#000; padding:0.2em 0.4em;">T22 Multitarget Tracking and Sensor Calibration in Centralized and Distributed Networks</h2>
 |-
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 <div id="tutorial23"></div>
-<!--        T23 Information fusion in resource-limited camera networks        -->
+<!--        T23 Information Fusion in Resource-Limited Camera Networks        -->
 | class="MainPageBG" style="width:100%; border:1px solid #bdd6c6; background:#e7f7e7; vertical-align:top; color:#000;" |
 {| id="mp-left" style="width:100%; vertical-align:top; background:#e7f7e76;"
-| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#d6efd6; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #bdd6c6; text-align:left; color:#000; padding:0.2em 0.4em;">T23 Information fusion in resource-limited camera networks</h2>
+| style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#d6efd6; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #bdd6c6; text-align:left; color:#000; padding:0.2em 0.4em;">T23 Information Fusion in Resource-Limited Camera Networks</h2>
 |-
 | style="color:#000;" | <div id="mp-tfa" style="padding:2px 5px">