Single-imager occupant detection based on surface reconstruction

This thesis introduces a novel framework for a real-time occupant detection system capable of extracting both two- and three-dimensional information using a single imager with active illumination. The primary objective of this thesis is to demonstrate the feasibility of such a low-cost classification system with comparable performance to multi-camera based stereo vision systems. Severe illumination conditions characterised by a frequent and wide illumination fluctuation are also challenging problems addressed in this work. The proposed system is designed to solve a problem of classifying three occupant classes being an adult, a forward-facing child seat, and a rear-facing child seat. DoubleFlash is employed to eliminate the influence of ambient illumination and to compress the optical dynamic range of target scenes. The idea underlying this technique is to subtract images flashed by different illumination power levels. The extension of this active illumination technique leads to the development of a novel shadow removal technique, called ShadowFlash. By simulating an artificial infinite illuminating plane over the field of view, the technique produces a shadowless scene without losing image details by composing multiple images illuminated from different directions. The ShadowFlash technique is then extended to the temporal domain by employing the sliding n-tuple strategy, which is introduced to avoid the reduction of the original frame rate. A modified active contour model, facilitated by morphological operations, extracts the boundary of the target object from the shadow-free scenes produced by the ShadowFlash. Based on the brightness information of the image triplet generated by the DoubleFlash, the orientations of the object surface at pixel points are estimated by the photometric stereo method and integrated into the 3D surface by means of global minimisation. The boundary information is used to specify the region of interest to reconstruct. Investigating both the two- and three-dimensional properties of vehicle occupants, 29 features are defined for the training of a neural network. The system is tested on a database of over 84,000 frames collected from a wide range of objects in various illumination conditions. A classification accuracy of 98.9% was achieved within the decision-time limit of three seconds

Meta Information in Graph-based Simultaneous Localisation And Mapping

Establishing the spatial and temporal relationships between a robot, and its environment serves as a basis for scene understanding. The established approach in the literature to simultaneously build a representation of the environment, and spatially and temporally localise the robot within the environment, is Simultaneous Localisation And Mapping (SLAM). SLAM algorithms in general, and in particular visual SLAM--where the primary sensors used are cameras--have gained a great amount of attention in the robotics and computer vision communities over the last few decades due to their wide range of applications. The advances in sensing technologies and image-based learning techniques provide an opportunity to introduce additional understanding of the environment to improve the performance of SLAM algorithms. In this thesis, I utilise meta information in a SLAM framework to achieve a robust and consistent representation of the environment and challenge some of the most limiting assumptions in the literature. I exploit structural information associated with geometric primitives, making use of the significant amount of structure present in real world scenes where SLAM algorithms are normally deployed. In particular, I exploit planarity of a group of points and introduce higher-level information associated with orthogonality and parallelism of planes to achieve structural consistency of the returned map. Separately, I also challenge the static world assumption that severely limits the deployment of autonomous mobile robotic systems in a wide range of important real world applications involving highly dynamic and unstructured environments by utilising the semantic and dynamic information in the scene. Most existing techniques try to simplify the problem by ignoring dynamics, relying on a pre-collected database of objects 3D models, imposing some motion constraints or fail to estimate the full SE(3) motions of objects in the scene which makes it infeasible to deploy these algorithms in real life scenarios of unknown and highly dynamic environments. Exploiting semantic and dynamic information in the environment allows to introduce a model-free object-aware SLAM system that is able to achieve robust moving object tracking, accurate estimation of dynamic objects full SE(3) motion, and extract velocity information of moving objects in the scene, resulting in accurate robot localisation and spatio-temporal map estimation

Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics

Three recent breakthroughs due to AI in arts and science serve as motivation: An award winning digital image, protein folding, fast matrix multiplication. Many recent developments in artificial neural networks, particularly deep learning (DL), applied and relevant to computational mechanics (solid, fluids, finite-element technology) are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. Hybrid methods combine traditional PDE discretizations with ML methods either (1) to help model complex nonlinear constitutive relations, (2) to nonlinearly reduce the model order for efficient simulation (turbulence), or (3) to accelerate the simulation by predicting certain components in the traditional integration methods. Here, methods (1) and (2) relied on Long-Short-Term Memory (LSTM) architecture, with method (3) relying on convolutional neural networks. Pure ML methods to solve (nonlinear) PDEs are represented by Physics-Informed Neural network (PINN) methods, which could be combined with attention mechanism to address discontinuous solutions. Both LSTM and attention architectures, together with modern and generalized classic optimizers to include stochasticity for DL networks, are extensively reviewed. Kernel machines, including Gaussian processes, are provided to sufficient depth for more advanced works such as shallow networks with infinite width. Not only addressing experts, readers are assumed familiar with computational mechanics, but not with DL, whose concepts and applications are built up from the basics, aiming at bringing first-time learners quickly to the forefront of research. History and limitations of AI are recounted and discussed, with particular attention at pointing out misstatements or misconceptions of the classics, even in well-known references. Positioning and pointing control of a large-deformable beam is given as an example.Comment: 275 pages, 158 figures. Appeared online on 2023.03.01 at CMES-Computer Modeling in Engineering & Science

Empowering Materials Processing and Performance from Data and AI

Third millennium engineering address new challenges in materials sciences and engineering. In particular, the advances in materials engineering combined with the advances in data acquisition, processing and mining as well as artificial intelligence allow for new ways of thinking in designing new materials and products. Additionally, this gives rise to new paradigms in bridging raw material data and processing to the induced properties and performance. This present topical issue is a compilation of contributions on novel ideas and concepts, addressing several key challenges using data and artificial intelligence, such as:- proposing new techniques for data generation and data mining;- proposing new techniques for visualizing, classifying, modeling, extracting knowledge, explaining and certifying data and data-driven models;- processing data to create data-driven models from scratch when other models are absent, too complex or too poor for making valuable predictions;- processing data to enhance existing physic-based models to improve the quality of the prediction capabilities and, at the same time, to enable data to be smarter; and- processing data to create data-driven enrichment of existing models when physics-based models exhibit limits within a hybrid paradigm

Identifying Structure Transitions Using Machine Learning Methods

Methodologies from data science and machine learning, both new and old, provide an exciting opportunity to investigate physical systems using extremely expressive statistical modeling techniques. Physical transitions are of particular interest, as they are accompanied by pattern changes in the configurations of the systems. Detecting and characterizing pattern changes in data happens to be a particular strength of statistical modeling in data science, especially with the highly expressive and flexible neural network models that have become increasingly computationally accessible in recent years through performance improvements in both hardware and algorithmic implementations. Conceptually, the machine learning approach can be regarded as one that employing algorithms that eschew explicit instructions in favor of strategies based around pattern extraction and inference driven by statistical analysis and large complex data sets. This allows for the investigation of physical systems using only raw configurational information to make inferences instead of relying on physical information obtained from a priori knowledge of the system. This work focuses on the extraction of useful compressed representations of physical configurations from systems of interest to automate phase classification tasks in addition to the identification of critical points and crossover regions