Sökning: "deep video models"

Visar resultat 1 - 5 av 7 avhandlingar innehållade orden deep video models.

  1. 1. Learning Spatiotemporal Features in Low-Data and Fine-Grained Action Recognition with an Application to Equine Pain Behavior

    Författare :Sofia Broomé; Hedvig Kjellström; Efstratios Gavves; KTH; []
    Nyckelord :ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Equine pain; computer vision for animals; deep learning; deep video models; spatiotemporal features; video understanding; action recognition; frame dependency; video data; end-to-end learning; temporal modeling; Datalogi; Computer Science;

    Sammanfattning : Recognition of pain in animals is important because pain compromises animal welfare and can be a manifestation of disease. This is a difficult task for veterinarians and caretakers, partly because horses, being prey animals, display subtle pain behavior, and because they cannot verbalize their pain. LÄS MER

  2. 2. Learning Representations for Segmentation and Registration

    Författare :Felix Järemo Lawin; Per-Erik Forssén; Michael Felsberg; Radu Patrice Horaud; Linköpings universitet; []
    Nyckelord :NATURAL SCIENCES; NATURVETENSKAP; NATURVETENSKAP; NATURAL SCIENCES; Computer Vision; point set registration; video object segmentation; time-of-flight; point set segmentation; deep learning; expectation maximization;

    Sammanfattning : In computer vision, the aim is to model and extract high-level information from visual sensor measurements such as images, videos and 3D points. Since visual data is often high-dimensional, noisy and irregular, achieving robust data modeling is challenging. LÄS MER

  3. 3. Automation of Navigation During the Short-loading Cycle Using Machine Vision

    Författare :Carl Borngrund; Ulf Bodin; Fredrik Sandin; Jerker Delsing; Henrik Andreasson; Luleå tekniska universitet; []
    Nyckelord :NATURAL SCIENCES; NATURVETENSKAP; NATURVETENSKAP; NATURAL SCIENCES; Automation; Short-loading cycle; Construction equipment; Deep Learning; Computer Vision; Cyberfysiska system; Cyber-Physical Systems;

    Sammanfattning : Earth-moving machines are machines used in a wide range of industries, such as the construction industry, to perform tasks related to earthworks.Currently, the vast majority of earth-moving machines are human-operated where expert operators perform these industry vital tasks. LÄS MER

  4. 4. Computer Vision for Automated Traffic Safety Assessment : A Machine Learning Approach

    Författare :Martin Ahrnbom; Mathematical Imaging Group; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Computer vision; Deep learning; Machine learning; Traffic Surveillance; Traffic Safety; Object detection; Camera Calibration; Multi-Target Tracking; Convolutional Neural Networks; Smart cities;

    Sammanfattning : Traffic safety is a complex and important research area with the potential to save many lives in the future. Two key problems are considered, namely the gathering of reliable and detailed road user statistics which can be used to estimate the safety of a traffic environment and taking advantage of surveillance infrastructure to guide and assist vehicles in real time, primarily autonomous ones. LÄS MER

  5. 5. Modeling of Magnetic Fields and Extended Objects for Localization Applications

    Författare :Niklas Wahlström; Fredrik Gustafsson; Thomas B. Schön; Simon Maskell; Linköpings universitet; []
    Nyckelord :ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Localization; magnetic tracking; extended target tracking; signal processing; machine learning; Gaussian processes; deep dynamical model; discretization;

    Sammanfattning : The level of automation in our society is ever increasing. Technologies like self-driving cars, virtual reality, and fully autonomous robots, which all were unimaginable a few decades ago, are realizable today, and will become standard consumer products in the future. LÄS MER