DroTrack: High-speed Drone-based Object Tracking Under Uncertainty

We present DroTrack, a high-speed visual single-object tracking framework for drone-captured video sequences. Most of the existing object tracking methods are designed to tackle well-known challenges, such as occlusion and cluttered backgrounds. The complex motion of drones, i.e., multiple degrees of freedom in three-dimensional space, causes high uncertainty. The uncertainty problem leads to inaccurate location predictions and fuzziness in scale estimations. DroTrack solves such issues by discovering the dependency between object representation and motion geometry. We implement an effective object segmentation based on Fuzzy C Means (FCM). We incorporate the spatial information into the membership function to cluster the most discriminative segments. We then enhance the object segmentation by using a pre-trained Convolution Neural Network (CNN) model. DroTrack also leverages the geometrical angular motion to estimate a reliable object scale. We discuss the experimental results and performance evaluation using two datasets of 51,462 drone-captured frames. The combination of the FCM segmentation and the angular scaling increased DroTrack precision by up to $9\%$ and decreased the centre location error by $162$ pixels on average. DroTrack outperforms all the high-speed trackers and achieves comparable results in comparison to deep learning trackers. DroTrack offers high frame rates up to 1000 frame per second (fps) with the best location precision, more than a set of state-of-the-art real-time trackers.Comment: 10 pages, 12 figures, FUZZ-IEEE 202

Mining Useful Information from Big Data Models Through Semantic-based Process Modelling and Analysis

Over the past few decades, most of the existing methods for analysing large growing knowledge bases, particularly Big Data, focus on building algorithms and/or technologies to help the knowledge-bases automatically or semi-automatically extend. Indeed, a vast number of such systems that construct the said large knowledge-bases continuously grow, and most often, they do not contain all of the facts about each process instance or elements that can be found within the process base. As a consequence, the resultant process models tend to be vague or missing value datasets. In view of such challenge, the work in this paper demonstrates that a well-designed information retrieval system or the process mining (PM) methods should present the results or discovered patterns in a formal and structured format qua being interpreted as domain knowledge. To this end, the work introduces a process mining approach that supports further enhancement of existing information systems or knowledge-base through the conceptual means of data analysis. In turn, the paper proposes a semantic-based process mining and analysis method, or better still, information retrieval and extraction system - that is capable of detecting patterns or unobserved behaviours within any given knowledge base by making use of the underlying semantics or properties (metadata) that describes the available data. Thus, the proposed approach is grounded on the semantic modelling and process mining techniques. The work illustrates this method using the case study of Learning Process. The goal is to discover user interaction patterns within a learning execution environment and respond by making decisions based on the semantical analysis of the captured users data. Practically, the method applies semantic annotation and ontological representation of the learning process domain data and the resultant models in order to discover patterns automatically by means of semantic reasoning. Theoretically, the process mining and modelling method show that a way of addressing the common challenge with computational intelligent systems or methods is through an effectively well-designed and fit for purpose system that meets the requirements and needs of the intended users. In other words, this paper applies effective reasoning methods to make inferences over a process knowledge-base (e.g. learning process) that leads to an automated discovery of learning patterns or behaviour

Technical report of data mining

No abstract availabl

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

Monitoring land use changes using geo-information : possibilities, methods and adapted techniques

Monitoring land use with geographical databases is widely used in decision-making. This report presents the possibilities, methods and adapted techniques using geo-information in monitoring land use changes. The municipality of Soest was chosen as study area and three national land use databases, viz. Top10Vector, CBS land use statistics and LGN, were used. The restrictions of geo-information for monitoring land use changes are indicated. New methods and adapted techniques improve the monitoring result considerably. Providers of geo-information, however, should coordinate on update frequencies, semantic content and spatial resolution to allow better possibilities of monitoring land use by combining data sets

Generating a Catalog of Unanticipated Schemas in Class Hierarchies using Formal Concept Analysis

International audienceContext: Inheritance is the cornerstone of object-oriented development, supporting conceptual modeling, subtype polymorphism and software reuse. But inheritance can be used in subtle ways that make complex systems hard to understand and extend, due to the presence of implicit dependencies in the inheritance hierarchy. Objective: Although these dependencies often specify well-known schemas (i.e., recurrent design or coding patterns, such as hook and template methods), new unanticipated dependency schemas arise in practice, and can consequently be hard to recognize and detect. Thus, a developer making changes or extensions to an object-oriented system needs to understand these implicit contracts defined by the dependencies between a class and its subclasses, or risk that seemingly innocuous changes break them. Method: To tackle this problem, we have developed an approach based on Formal Concept Analysis. Our FoCARE methodology (Formal Concept Analysis based-Reverse Engineering) identifies undocumented hi- erarchical dependencies in a hierarchy by taking into account the existing structure and behavior of classes and subclasses. Results: We validate our approach by applying it to a large and non-trivial case study, yielding a catalog of Hierarchy Schemas, each one composed of a set of dependencies over methods and attributes in a class hierarchy. We show how the discovered dependency schemas can be used not only to identify good design practices, but also to expose bad smells in design, thereby helping developers in initial reengineering phases to develop a first mental model of a system. Although some of the identified schemas are already documented in existing literature, with our approach based on Formal Concept Analysis (FCA), we are also able to identify previously unidentified schemas

Consciousness CLEARS the Mind

A full understanding of consciouness requires that we identify the brain processes from which conscious experiences emerge. What are these processes, and what is their utility in supporting successful adaptive behaviors? Adaptive Resonance Theory (ART) predicted a functional link between processes of Consciousness, Learning, Expectation, Attention, Resonance, and Synchrony (CLEARS), includes the prediction that "all conscious states are resonant states." This connection clarifies how brain dynamics enable a behaving individual to autonomously adapt in real time to a rapidly changing world. The present article reviews theoretical considerations that predicted these functional links, how they work, and some of the rapidly growing body of behavioral and brain data that have provided support for these predictions. The article also summarizes ART models that predict functional roles for identified cells in laminar thalamocortical circuits, including the six layered neocortical circuits and their interactions with specific primary and higher-order specific thalamic nuclei and nonspecific nuclei. These prediction include explanations of how slow perceptual learning can occur more frequently in superficial cortical layers. ART traces these properties to the existence of intracortical feedback loops, and to reset mechanisms whereby thalamocortical mismatches use circuits such as the one from specific thalamic nuclei to nonspecific thalamic nuclei and then to layer 4 of neocortical areas via layers 1-to-5-to-6-to-4.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624