Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

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The Parameter Houlihan: a solution to high-throughput identifiability indeterminacy for brutally ill-posed problems

One way to interject knowledge into clinically impactful forecasting is to use data assimilation, a nonlinear regression that projects data onto a mechanistic physiologic model, instead of a set of functions, such as neural networks. Such regressions have an advantage of being useful with particularly sparse, non-stationary clinical data. However, physiological models are often nonlinear and can have many parameters, leading to potential problems with parameter identifiability, or the ability to find a unique set of parameters that minimize forecasting error. The identifiability problems can be minimized or eliminated by reducing the number of parameters estimated, but reducing the number of estimated parameters also reduces the flexibility of the model and hence increases forecasting error. We propose a method, the parameter Houlihan, that combines traditional machine learning techniques with data assimilation, to select the right set of model parameters to minimize forecasting error while reducing identifiability problems. The method worked well: the data assimilation-based glucose forecasts and estimates for our cohort using the Houlihan-selected parameter sets generally also minimize forecasting errors compared to other parameter selection methods such as by-hand parameter selection. Nevertheless, the forecast with the lowest forecast error does not always accurately represent physiology, but further advancements of the algorithm provide a path for improving physiologic fidelity as well. Our hope is that this methodology represents a first step toward combining machine learning with data assimilation and provides a lower-threshold entry point for using data assimilation with clinical data by helping select the right parameters to estimate

Automotive Interior Sensing - Temporal Consistent Human Body Pose Estimation

Com o surgimento e desenvolvimento de veículos autónomos, surgiu igualmente uma necessidade de monitorizar e identificar objetos e ações que ocorrem no ambiente que rodeia o veículo. Este tipo de monitorização é particularmente importante no caso de veículos partilhados, dada a necessidade de identificar ações não só no exterior mas também no interior do veículo devido à ausência de um condutor humano que possa detetar, por exemplo, potenciais ações de violência entre passageiros e/ou situações onde estes necessitem de assistência. Englobado neste contexto, a Bosch desenvolveu uma solução de estimação de postura humana com o objetivo de extrapolar a pose de todos os ocupantes presentes numa dada imagem, inferir o comportamento de cada passageiro e, consequentemente, identificar ações potencialmente maliciosas. Porém, para que este algoritmo possa ser aplicado não apenas a imagens isoladas mas também a vídeos é necessário adicionar contexto temporal entre frames. Por outras palavras, é necessário associar a estimação de pose de uma dada pessoa para uma dada frame às estimações de pose para a mesma pessoa em frames subsequentes de modo a que a identificação dessa pessoa (ou qualquer outra presente numa dada frame) ao longo do vídeo seja correta e consistente. O tópico de associação temporal, também conhecido como "pose tracking", é abordado e desenvolvido ao longo do presente projeto, culminando na proposta e implementação de uma solução que melhora consideravelmente a consistência temporal do algoritmo de estimação de pose humana da Bosch. A solução desenvolvida utiliza uma mistura de abordagens clássicas e atuais de associação de informação, como por exemplo o "Hungarian algorithm" e "Intersection over Union", e abordagens de lógica de informação desenvolvidas especificamente para o caso em questão. A performance do algoritmo implementado no presente projeto é avaliada usando duas das mais recorrentes métricas de avaliação em casos de rastreamento de pose.With the emergence and development of autonomous vehicles, a necessity to constantly monitor and identify objects and action that occur in the surrounding environment of the vehicle itself was also created. This type of monitoring is particularly important in the case of shared vehicles, given the necessity to identify actions not only in the exterior but also in the interior of the vehicle due to the absence of a human driver that can detect, for instance, potential violent actions between passengers and/or cases where assistence is required. Encompassed in this context, Bosch has developed a human body pose estimation solution in order to extrapolate the pose of all vehicle occupants present in a given image, infere the behaviour of each passenger and, consequently, identify potentially malicious actions. However, in order to apply this algorithm not only to isolated images but also to videos it is necessary to add temporal context between frames. In other words, an association is required between the body pose estimation for a given person in a given frame and the body pose estimations for the same person in subsequent frames in order to ensure that the identification of that passenger (or any other passenger present in the same frame) is accurate and consistent throughout the entire video. The temporal association topic, also known as pose tracking, is addressed and developed during the present project, culminating in the proposal and implementation of a solution that considerably improves the temporal consistency of the human body pose estimation algorithm developed by Bosch. The implemented solution uses a mixture of currently relevant classical approaches for data association, such as the Hungarian algorithm e Intersection over Union techniques, and approaches based on data logic developed specifically for the present case. Regarding performance, the developed algorithm is evaluated using two of the most recurrent metrics for pose tracking methods

Dynamic Data Assimilation

Data assimilation is a process of fusing data with a model for the singular purpose of estimating unknown variables. It can be used, for example, to predict the evolution of the atmosphere at a given point and time. This book examines data assimilation methods including Kalman filtering, artificial intelligence, neural networks, machine learning, and cognitive computing

Language Identification Using Visual Features

Automatic visual language identification (VLID) is the technology of using information derived from the visual appearance and movement of the speech articulators to iden- tify the language being spoken, without the use of any audio information. This technique for language identification (LID) is useful in situations in which conventional audio processing is ineffective (very noisy environments), or impossible (no audio signal is available). Research in this field is also beneficial in the related field of automatic lip-reading. This paper introduces several methods for visual language identification (VLID). They are based upon audio LID techniques, which exploit language phonology and phonotactics to discriminate languages. We show that VLID is possible in a speaker-dependent mode by discrimi- nating different languages spoken by an individual, and we then extend the technique to speaker-independent operation, taking pains to ensure that discrimination is not due to artefacts, either visual (e.g. skin-tone) or audio (e.g. rate of speaking). Although the low accuracy of visual speech recognition currently limits the performance of VLID, we can obtain an error-rate of < 10% in discriminating between Arabic and English on 19 speakers and using about 30s of visual speech

Fast, collaborative acquisition of multi-view face images using a camera network and its impact on real-time human identification

Biometric systems have been typically designed to operate under controlled environments based on previously acquired photographs and videos. But recent terror attacks, security threats and intrusion attempts have necessitated a transition to modern biometric systems that can identify humans in real-time under unconstrained environments. Distributed camera networks are appropriate for unconstrained scenarios because they can provide multiple views of a scene, thus offering tolerance against variable pose of a human subject and possible occlusions. In dynamic environments, the face images are continually arriving at the base station with different quality, pose and resolution. Designing a fusion strategy poses significant challenges. Such a scenario demands that only the relevant information is processed and the verdict (match / no match) regarding a particular subject is quickly (yet accurately) released so that more number of subjects in the scene can be evaluated.;To address these, we designed a wireless data acquisition system that is capable of acquiring multi-view faces accurately and at a rapid rate. The idea of epipolar geometry is exploited to get high multi-view face detection rates. Face images are labeled to their corresponding poses and are transmitted to the base station. To evaluate the impact of face images acquired using our real-time face image acquisition system on the overall recognition accuracy, we interface it with a face matching subsystem and thus create a prototype real-time multi-view face recognition system. For front face matching, we use the commercial PittPatt software. For non-frontal matching, we use a Local binary Pattern based classifier. Matching scores obtained from both frontal and non-frontal face images are fused for final classification. Our results show significant improvement in recognition accuracy, especially when the front face images are of low resolution

The future of social is personal: the potential of the personal data store

This chapter argues that technical architectures that facilitate the longitudinal, decentralised and individual-centric personal collection and curation of data will be an important, but partial, response to the pressing problem of the autonomy of the data subject, and the asymmetry of power between the subject and large scale service providers/data consumers. Towards framing the scope and role of such Personal Data Stores (PDSes), the legalistic notion of personal data is examined, and it is argued that a more inclusive, intuitive notion expresses more accurately what individuals require in order to preserve their autonomy in a data-driven world of large aggregators. Six challenges towards realising the PDS vision are set out: the requirement to store data for long periods; the difficulties of managing data for individuals; the need to reconsider the regulatory basis for third-party access to data; the need to comply with international data handling standards; the need to integrate privacy-enhancing technologies; and the need to future-proof data gathering against the evolution of social norms. The open experimental PDS platform INDX is introduced and described, as a means of beginning to address at least some of these six challenges

People detection, tracking and biometric data extraction using a single camera for retail usage

Tato práce se zabývá návrhem frameworku, který slouží k analýze video sekvencí z RGB kamery. Framework využívá technik sledování osob a následné extrakce biometrických dat. Biometrická data jsou sbírána za účelem využití v malobochodním prostředí. Navržený framework lze rozdělit do třech menších komponent, tj. detektor osob, sledovač osob a extraktor biometrických dat. Navržený detektor osob využívá různé architektury sítí hlubokého učení k určení polohy osob. Řešení pro sledování osob se řídí známým postupem \uv{online tracking-by-detection} a je navrženo tak, aby bylo robustní vůči zalidněným scénám. Toho je dosaženo začleněním dvou metrik týkající se vzhledu a stavu objektu v asociační fázi. Kromě výpočtu těchto deskriptorů, jsme schopni získat další informace o jednotlivcích jako je věk, pohlaví, emoce, výška a trajektorie. Návržené řešení je ověřeno na datasetu, který je vytvořen speciálně pro tuto úlohu.This thesis proposes a framework that analyzes video sequences from a single RGB camera by extracting useful soft-biometric data about tracked people. The aim is to focus on data that could be utilized in a retail environment. The designed framework can be broken down into the smaller components, i.e., people detector, people tracker, and soft-biometrics extractor. The people detector employs various deep learning architectures that estimate bounding boxes of individuals. The tracking solution follows the well-known online tracking-by-detection approach, while the proposed solution is built to be robust regarding the crowded scenes by incorporating appearance and state features in the matching phase. Apart from calculating appearance descriptors only for matching, we extract additional information of each person in the form of age, gender, emotion, height, and trajectory when possible. The whole framework is validated against the dataset which was created for this propose