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Image Analysis Using Human Body Geometry and Size Proportion Science for Action Classification

  • 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,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.show moreshow less

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    Gefördert durch das Programm Open Access Publizieren der DFG und den Publikationsfonds der Bauhaus-Universität Weimar.

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Metadaten
Document Type:Article
Author: Syed Muhammad Saqlai, Anwar GhaniORCiD, Imran KhanORCiD, Shahbaz Ahmed Khan Ghayyur, Shahaboddin ShamshirbandORCiD, Narjes NabipourORCiD, Manouchehr ShokriORCiD
DOI (Cite-Link):https://doi.org/10.3390/app10165453Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20200904-42322Cite-Link
URL:https://www.mdpi.com/2076-3417/10/16/5453
Parent Title (German):Applied Sciences
Publisher:MDPI
Place of publication:Basel
Language:English
Date of Publication (online):2020/08/07
Year of first Publication:2020
Release Date:2020/09/04
Publishing Institution:Bauhaus-Universität Weimar
Institutes:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Volume:2020
Issue:volume 10, issue 16, article 5453
Pagenumber:24
Tag:action recognition; human blob; human body proportions; rule based classification
GND Keyword:Bildanalyse; Mensch; Größenverhältnis; Geometrie; Körper
Dewey Decimal Classification:000 Informatik, Informationswissenschaft, allgemeine Werke
BKL-Classification:33 Physik
54 Informatik
Open Access Publikationsfonds:Open-Access-Publikationsfonds 2020
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)