Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Drowning Detection in swimming pool is a challenging problem in recent times for which no satisfiable solutions have been set up. As of known methods primarily rely on background subtraction-based technique. However, random motion caused by water rippling, splashing, and moving object and reflections frequently result in interface and inaccuracies. In this work, an alternative for real time drowning detection solution by using deep learning technology will be given. This method uses convolution neural network object detector to generate confidence maps of object location in pool. If the swimmer or object get strain then the system detects it by motion
Keywords:
Drowning Detection, You Only Look Once (YOLO), alarm
Cite Article:
"Life-Guard for drowning detection", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 5, page no.2071 - 2073, May-2023, Available :http://www.ijrti.org/papers/IJRTI2305191.pdf
Downloads:
000205295
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator