<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/10013856" mdate="2024-10-21 00:00:00"> <author>Kaushik Sathupadi and Sandesh Achar</author> <title>3D Human Reconstruction over Cloud Based Image Data via AI and Machine Learning</title> <pages>644 - 650</pages> <year>2024</year> <volume>18</volume> <number>10</number> <journal>International Journal of Computer and Information Engineering</journal> <ee>https://publications.waset.org/pdf/10013856</ee> <url>https://publications.waset.org/vol/214</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>Human action recognition (HAR) modeling is a critical task in machine learning. These systems require better techniques for recognizing body parts and selecting optimal features based on vision sensors to identify complex action patterns efficiently. Still, there is a considerable gap and challenges between images and videos, such as brightness, motion variation, and random clutters. This paper proposes a robust approach for classifying human actions over cloudbased image data. First, we apply preprocessing and detection, human and outer shape detection techniques. Next, we extract valuable information in terms of cues. We extract two distinct features fuzzy local binary patterns and sequence representation. Then, we applied a greedy, randomized adaptive search procedure for data optimization and dimension reduction, and for classification, we used a random forest. We tested our model on two benchmark datasets, AAMAZ and the KTH Multiview Football datasets. Our HAR framework significantly outperforms the other stateoftheart approaches and achieves a better recognition rate of 91 and 89.6 over the AAMAZ and KTH Multiview Football datasets, respectively.</abstract> <index>Open Science Index 214, 2024</index> </article>