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Fusion 2017 Special Session Proposal Submissions

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List of Special Sessions of FUSION 2017

SS1:Big Data Information Fusion with the Theory of Belief Functions

Description: Big data has gained become a hotspot in both theoretical research and real applications in the latest 10 years due to the rapid expansion of the massive amount of data that is available for solving different tasks within many different application domains. Big data is so large or complex that traditional data processing applications are inadequate to deal with them. One significant aspect is the uncertainty incorporated in the Big data.
The theory of belief function is a powerful tool for uncertainty modeling and reasoning, which is expected to provide effective solutions for the Big data processing and analyses in information fusion related applications. Therefore, we propose to organize such a special session including the subtopics as follows:

  • Belief function based analysis of complex networks
  • Belief function based clustering analysis
  • Belief function based pattern classification
  • Belief function based feature selection/extraction
  • Other topics in belief function based applications in information fusion

Organizers: Arnaud Martin (Arnaud.Martin@univ-rennes1.fr ) and Deqiang Han (deqhan@mail.xjtu.edu.cn)

SS2: Information Fusion in Multi-Biometrics and Forensics

Description: This session will focus on the latest innovations and best practices in the emerging field of multi-biometric fusion. Biometrics tries to build an identity recognition decision based on the physical or behavioral characteristics of individuals. Multi-biometrics aims at outperforming the conventional biometric solutions by increasing accuracy, and robustness to intra-person variations and to noisy data. It also reduces the effect of the non-universality of biometric modalities and the vulnerability to spoof attacks. Fusion is performed to build a unified biometric decision based on the information collected from different biometric sources. This unified result must be constructed in a way that guarantees the best performance possible and take into account the efficiency of the solution.
The topic of this special session, Information Fusion in Multi-Biometrics and Forensics, requires the development of innovative and diverse solutions. Those solutions must take into account the nature of biometric information sources as well as the level of fusion suitable for the application in hand. The fused information may include more general and non-biometric information such as the estimated age of the individual or the environment of the background.
This special session will be supported by the European Association for Biometrics (EAB). The EAB will provide technical support by addressing experts for reviews and will help with the dissemination and exploitation of the event.

Organizers: Naser Damer (naser.damer@igd.fraunhofer.de) and Raghavendra Ramachandra (raghavendra.ramachandra@ntnu.no)

SS3: Intelligent Information Fusion

Description: Research on intelligent systems for information fusion has matured during the last years and many effective applications of this technology are now deployed. The problem of Information Fusion has attracted significant attention in the artificial intelligence and machine learning community, trying to innovate in the techniques used for combining the data and to provide new models for estimations and predictions. The rapid growing advances of Information Fusion accompanied with the advances of sensor technologies and distributed computing systems has led to new applications in different environments such as Wearable Computing, Intelligent Surveillance, Air Traffic Management, Smart City/Home Care, Smart Grid, Web Tracking, Network Management, etc. With the continuing expansion of the domain of interest andthe increasing complexity of the collected information, intelligent techniques for information processing have become a crucial component to support high level decision making and management.

Organizers: Juan Manuel Corchado (corchado@usal.es ) Javier Bajo (jbajo@fi.upm.es) and Tiancheng Li (t.c.li@usal.es)

SS4: Target Motion Analysis and Passive Tracking

Description: Target Motion Analysis (TMA) is an estimation process to determine the target trajectories using single/multiple passive sensor (e.g. passive SONAR and RADAR) detections. These passive sensors dedicate to ”°listening”± to the target emitted signals or the other emitter signals via targets, without emitting signals themselves. Due to the concealing and environment-friendly properties of passive sensors, TMA has wide civil and military applications, for example underwater and surface surveillance, submarine command and control system and air defense. As the passive sensors cannot provide direct target location measurements, TMA suffers from poor target trajectory estimation accuracy due to marginal observability from sensor measurements. This creates a gap between the operational needs and the existing TMA approaches, which induces research interests for many years. This special session aims to gather researchers interested in TMA topic to present and share their latest research results, and discuss the tough issues and future potential in the TMA, such as propagation delay effect, emitter position uncertainty of multistate systems, contextual information-aided tracking, new configuration, etc.

Organizers: Gee Wah Ng (ngeewah@dso.org.sg ) and Claude Jauffret (jauffret@univ-tln.fr)

SS5: Multi-Sensor Data Fusion for Intelligent Vehicles

Description: During the past decades, intelligent vehicles (ADAS and autonomous driving) have obtained more and more attentions and developments from both research society and industry community. One of the necessary components to develop ADAS systems and/or driverless cars is perception of the surrounding vehicle environment. In intelligent vehicles, perception systems are able to sense and interpret surrounding environment based on various kinds of sensors, such as radar, sonar sensors, 2D/3D lidar, monocular/binocular/omnidirectional/RGB-D vision system, inertial sensors, etc. The perception systems provide and process sensed information for representing dynamically the content of the surrounding environment (detection, tracking and recognition of static and dynamic objects). Therefore, they are of the most importance in intelligent vehicles since their outputs are required to make decision for driver assistance and/or vehicle control in complex environment. This special section aims at exhibiting the latest research achievements in intelligent vehicles. The proposed themes include (but are not limited to):

  • Architectures supporting modular/plug&play sensors for intelligent vehicles
  • Distributed data fusion for intelligent vehicles and connected infrastructure
  • Multi-target Multi-source tracking and filtering
  • Multi-Sensor SLAM
  • Adaptable spatio-temporal sensor registration on intelligent vehicles
  • The industry perspective

Organizers: Feihu Zhang (feihu.zhang@nwpu.edu.cn ), You Li (you.li@renault.com), Dongpu Cao (d.cao@cranfield.ac.uk),and Alois Knoll (Knoll@in.tum.de)

SS6: Sensor Data Mining For Tracking

Description: The rapid development of advanced sensors and their joint application provide a foundation for new paradigms to combat the challenges that arise in target detection, tracking and forecasting in harsh environments with poor prior information. As a consequence, the sensor community has expressed interest in novel data mining methods coupling traditional statistical techniques for substantial performance enhancement. For example, the advent of multiple/massive sensor systems provides very rich observation at high frequency yet low financial cost, which facilitates novel perspectives based on data clustering and model learning to deal with false alarms and misdetection, given little statistical knowledge about the objects, sensors and the background. Numerical fitting and regression analysis provide another unlimited means to utilize the unstructured context information such as ”°the trajectory is smooth”± for continuous-time target trajectory estimation. Probabilistic modelling and learning offers interesting possibilities for systematic representation of uncertainty based on the probability theory.
Incorporating additional, readily available information to constrain the adaptive response and to combat poor scenario knowledge, has shown promise as a means of restoring sensor capability over a range of challenging operating conditions as well as to deal with a variety of challenging problems that makes traditional approaches awkward. The purpose of this special section is to assemble and disseminate information on recent, novel advances in sensor signal and data mining techniques and approaches, and promote a forum for continued discussion on the future development. Both theoretical and practical approaches in the area are welcomed.

Organizers: Tiancheng Li (t.c.li@usal.es ) Haibin Ling (hbling@temple.edu) and Genshe Chen (gchen@intfusiontech.com)

SS7: High-dimensional and Deep Representation Methods for Information Fusion

Description: With the development of hardware, large amounts of data with different attributes can be obtained. This leads to advantages such as simultaneous coverage of large environments, increased resolution, redundancy, multimodal scene information and robustness against occlusion. Unfortunately, multiple challenges should be tackled at first, like registration and fusion, such as to explore these benefits. These difficulties in turn raise the question and re-understanding of how do we describe the high-dimensional and dynamic data in a more effective and practical fashion.
Generally, techniques in image processing and computer vision community develop from pixel-based methods with predefined transform and patch-based methods with adaptive sparse representation to the emerging high-dimensional and deep representation. The proposed special session is to address challenging problems in image registration, image fusion and their applications for high-dimensional systems and systems with complex dynamics with high-dimensional and deep representations. This session will get together experts from different applications and aims at presenting novel techniques, algorithms, approaches on high-dimensional representation and deep learning methods. Both theoretically oriented and application related works are welcomed.

Organizers: Qiegen Liu (qiegen.liu@ucalgary.ca ) Henry Leung (leungh@ucalgary.ca) ,Yong Xia (yxia@nwpu.edu.cn)and Yu Liu (yuliu@hfut.edu.cn)

SS8: Applications of Data Analytics and Information Fusion to Finance, Business, and Marketing

Description: This proposal is to propose a special session of applying data fusion and predictive analytics to finance, business, and marketing within the Fusion 2017 conference. Finance and business are critical application areas in information fusion and data analytics. Many of the techniques discussed in the information fusion community are directly applicable to this emerging and important application area. The goal of this proposed session is to open up a forum for data scientists and engineer to share their latest experience and insight on applying the predictive modeling and data analytics techniques to the applications in finance and business areas.
Topics include but are not limited to:

  • Predictive marketing
  • Predictive model and decision analytics, market forecasting
  • Fundamental trading strategies with data fusion techniques
  • Bayesian networks, neural networks, deep learning, rule-based, ontologies
  • Technical trading strategies and performance evaluation
  • Kalman filtering, multiple switching models, machine learning, dynamic asset allocation, portfolio management
  • Economic data analytics and Business forecasting
  • Financial and risk analysis, data fusion for business intelligence and decision

Organizers:KC Chang(kchang@gmu.edu ) and Zhi Tian (ztian1@gmu.edu)

SS9: Evaluation of Technologies for Uncertainty Reasoning

Description: The session will focus three topics: (1) to summarize the state of the art in uncertainty analysis, representation, and evaluation, (2) discussion of metrics for uncertainty representation, and (3) survey uncertainty at all levels of fusion. The impact to the ISIF community would be an organized session with a series of methods in uncertainty representation as coordinated with evaluation.
The techniques discussed and questions/answers would be important for the researchers in the ISIF community; however, the bigger impact would be for the customers of information fusion systems to determine how measure, evaluate, and approve systems that assess the situation beyond Level 1 fusion.
The customers of information fusion products would have some guidelines to draft requirements documentation, the gain of fusion systems over current techniques, as well as issues that important in information fusion systems designs. One of the main goals of information fusion is uncertainty reduction, which is dependent on the representation chosen. Uncertainty representation differs across the various levels of Information Fusion (as defined by the JDL/DFIG models). Given the advances in information fusion systems, there is a need to determine how to represent and evaluate situational (Level 2 Fusion),
impact (Level 3 Fusion) and process refinement (Level 5 Fusion), which is not well standardized for the information fusion community.

Organizers: Paulo Costa, Kathryn Laskey, Anne-Laure Jousselme and Pieter DeVilliers

SS10: Extended Object and Group Tracking

Description: Typical object tracking algorithms assume that the object can be modeled as a single point without an extent. However, there are many scenarios in which this assumption is not reasonable. For example, when the resolution of the sensor device is higher than the spatial extent of the object, a varying number of measurements from spatially distributed reflection centers are received. Furthermore, a collectively moving group of point objects can be seen as a single extended object because of the interdependency of the group members.
This Special Session addresses fundamental techniques, recent developments and future research directions in the field of extended object and group tracking.
Topics of this Special Session may include, but are not limited to
Methodologies for tracking extended objects and groups: Bayesian inference, nonlinear filtering, random sets, data association
Sensors models:
Radar devices, laser range finders, RGB cameras, depth cameras
Applications:
Navigation, robotics, medicine, biology, autonomous driving,
surveillance, SLAM
Case studies:
Benchmark scenarios, performance measures, experiments, simulations

Organizers: Marcus Baum (marcus.baum@cs.uni-goettinge.de), Uwe D. Hanebeck (uwe.hanebeck@kit.edu), Peter Willett (peter.k.willett@gmail.com) and Wolfgang Koch(wolfgang.koch@fkie.fraunhofer.de)

SS11: Environment Perception with Applications to Automotive Driving

Description: This special session focuses on the fusion applications related to autonomous vehicles. Topics may include but not limited to the following:

  • Estimation for extended target tracking using image, radar, or Lidar
  • Sensor (measurement) modeling for extended target tracking
  • Multiple extended target tracking
  • Fusion of extended target estimation with heterogeneous sensors
  • SLAM
  • Target detection and classification
  • Case study or special application to autonomous driving, etc

Organizers: Yu Liu (aliuyu18@gmail.com) and Jian Lan (lanjian@xjtu.edu.cn)

SS12: Directional Estimation

Description:This Special Session addresses fundamental techniques, recent developments, and future research directions in the field of estimation involving directional and periodic data. It is our goal to bridge the gap between theoreticians and practitioners. Thus, we welcome both applied and theoretic contributions to this topic.
Topics of interest:

  • Estimation of circular or directional quantities
  • Combination of periodic and linear quantities, e.g., for 6 DOF pose estimation
  • Circular and directional statistics
  • Statistics on the rotation groups SO(2) and SO(3), the Euclidean Group SE(2) and SE(3), and other manifolds
  • Recursive and batch filtering in a periodic setting
  • Applications: tracking, robotics, medicine, biology

Organizers: Gerhard Kurz (gerhard.kurz@kit.edu), Igor Gilitschenski (igilitschenski@ethz.ch), Florian Pfaff (florian.pfaff@kit.edu), and Uwe D. Hanebeck (uwe.hanebeck@ieee.org)

SS13: Homotopy Methods for Progressive Bayesian Estimation

Description:This session is concerned with homotopy methods for the efficient solution of Bayesian state estimation problems occurring in information fusion and filtering. For state estimation in the presence of stochastic uncertainties, the best current estimate is represented by a probability density function.
For that purpose, different representations are used including continuous densities such as Gaussian mixtures or discrete densities on continuous domain such as particle sets. Given prior knowledge in form of such a density, the goal is to include new information by means of Bayes' theorem. Typically, the resulting posterior density is of higher complexity and difficult to compute. In the case of particle sets, additional problems such as particle degeneracy occur. Hence, an appropriate approximate posterior has to be found. For recursive applications, this approximate posterior should be of the same form as the given prior density (approximate closedness). To cope with this challenging approximation problem, a well-established technique is to gradually include the new information instead of using it in one shot, which is achieved by a homotopy.
For this session, manuscripts are invited that cover any aspect of homotopy methods for state estimation. This includes both theoretically oriented work and applications of known methods.
Topics of this Special Session may include, but are not limited to

  • Homotopy-based estimation methods for continuous and discrete densities
  • Derivations of flow regimes
  • Specific homotopy schedules
  • Modification of representation capabilities during flow
  • New ideas on processing details
  • Comparisons of existing methods
  • Applications of homotopy estimation

Organizers: Christof Chlebek (christof.chlebek@kit.edu), Uwe D. Hanebeck (uwe.hanebeck@ieee.org) and Fred Daum (Frederick_E_Daum@raytheon.com)

SS14: Big Data Fusion and Analytics

Description: Big data has tremendous potential to transform private sector businesses and defense organizations with valuable strategic information and actionable intelligence and patterns. International Data Corporation (IDC) forecasts that big data and business analytics applications, tools, and services will grow to $187 billion in 2019. But the volume, veracity and velocity, the three major aspects that typically characterize a big data environment, pose significant challenge in searching, processing, and extracting intelligence for strategic situational awareness and decision support.
This special session will focus on fusion and analytics (two sides of the same coin!) to process big centralized data, inherently distributed data, and data residing on the cloud. The fusion and analytics techniques to be covered will handle structured and/or unstructured data. Structured data refers to computerized information which can be easily interpreted and used by a computer program supporting a range of tasks. For example, the information stored in a relational database is structured, whereas text documents, videos, and images are usually unstructured.
Potential authors from both academia and industry are encouraged to submit papers on the following topics:

  • High-level big data situation and threat assessment.
  • Descriptive and predictive big data analytics.
  • Text analytics of unstructured HUMINT, textual blogs, emails, surveys, etc., via deep linguistics processing.
  • Cloud computing for fusion and analytics using relevant platforms and paradigms such as Hadoop, MapReduce, and Accumulo, and scripting languages such as R and Python.
  • Big data search and query in traditional and NoSQL environments.
  • Distributed fusion and analytics for inherently distributed big data.
  • Deep learning of neural and belief networks.
  • Tracking and resource management using big data.
  • Prescriptive analytics and decision support in the presence of big data.
  • Sampling based approach to fusion and analytics in big data environments.
  • Distributed model-based computation.
  • Data mining and knowledge discovery in big data residing in cloud and warehouses.
  • Extraction of structured information from textual and other types of unstructured data.
  • Handling of uncertainly in big data environment.

The session is aimed to be both practical and discussion oriented. Demos are welcome as part of the talk.

Organizers: Subrata Das (sdas@machineanalytics.com) and Arup Das (adas@alphaserveit.com)

SS15: Advances in Distributed Kalman Filtering and Fusion

Description: The rapid advances in sensor and communication technologies are accompanied by an increasing demand for distributed state estimation methods. Centralized implementations of the Kalman filter are often too costly in terms of communication bandwidth or simply inapplicable - for instance when mobile ad-hoc networks are considered. Compared with centralized approaches, distributed or decentralized Kalman filtering is considerably more elaborate. In particular, the treatment of dependent information shared by different systems is a key issue. Distributed state estimation is, in general, a balancing act between estimation quality and flexible network design. Although distributed implementations of the Kalman filter that provide optimal estimates are possible, these algorithms are not robust to packet delays and drops, node failures, and changing network topologies. In practice, these problems deserve careful attention and have to be addressed by future research. The objective of this special session is to bring together researchers who are concerned with distributed state estimation problems.

Organizers: Benjamin Noack (noack@kit.edu), Florian Pfaff (florian.pfaff@kit.edu), Felix Govaers (felix.govaers@fkie.fraunhofer.de), Uwe D. Hanebeck (uwe.hanebeck@ieee.org) and Wolfgang Koch (wolfgang.koch@fkie.fraunhofer.de)