000 Informatik, Informationswissenschaft, allgemeine Werke
Atlas der Datenkörper. Körperbilder in Kunst, Design und Wissenschaft im Zeitalter digitaler Medien
(2022)
Digitale Technologien und soziale Medien verändern die Selbst- und Körperwahrnehmung und verzerren, verstärken oder produzieren dabei spezifische Körperbilder. Die Beiträger*innen kartographieren diese Phänomene, fragen nach ihrer medialen Existenzweise sowie nach den Möglichkeiten ihrer Kritik. Dabei begegnen sie ihrer Neuartigkeit mit einer transdisziplinären Herangehensweise. Aus sowohl der Perspektive künstlerischer und gestalterischer Forschung als auch der Kunst-, Kultur- und Medienwissenschaft sowie der Psychologie und Neurowissenschaft wird die Landschaft rezenter Körperbilder und Techniken einer digitalen Körperlichkeit untersucht.
Image Analysis Using Human Body Geometry and Size Proportion Science for Action Classification
(2020)
Gestures are one of the basic modes of human communication and are usually used to represent different actions. Automatic recognition of these actions forms the basis for solving more complex problems like human behavior analysis, video surveillance, event detection, and sign language recognition, etc. Action recognition from images is a challenging task as the key information like temporal data, object trajectory, and optical flow are not available in still images. While measuring the size of different regions of the human body i.e., step size, arms span, length of the arm, forearm, and hand, etc., provides valuable clues for identification of the human actions. In this article, a framework for classification of the human actions is presented where humans are detected and localized through faster region-convolutional neural networks followed by morphological image processing techniques. Furthermore, geometric features from human blob are extracted and incorporated into the classification rules for the six human actions i.e., standing, walking, single-hand side wave, single-hand top wave, both hands side wave, and both hands top wave. The performance of the proposed technique has been evaluated using precision, recall, omission error, and commission error. The proposed technique has been comparatively analyzed in terms of overall accuracy with existing approaches showing that it performs well in contrast to its counterparts.