Human Verification over Activity Analysis via Deep Data Mining

Human verification and activity analysis (HVAA) are primarily employed to observe, track, monitor human motion patterns using red-green-blue (RGB) images videos. Interpreting interaction RGB is one of the most complex machine learning tasks in recent times. Numerous models rely on various parameters, such as detection rate, position, direction body components images. This paper presents robust for event recognition via extraction contextual intelligence-based features. To use image sequences input data, we first perform a few denoising steps. Then, human-to-human analyses deliver more precise results. phase follows feature engineering techniques, including diverse selection. Next, used graph mining method optimization AdaBoost classification. We tested our proposed HVAA model two benchmark datasets. The testing system exhibited mean accuracy 92.15% Sport Videos Wild (SVW) dataset. second dataset, UT-interaction, had 92.83%. Therefore, these results demonstrated better rate outperformed other novel techniques part tracking detection. can be utilized numerous real-world applications including, healthcare, surveillance, task monitoring, atomic actions, gesture posture analysis.