Recognition of human action for scene understanding using world cup optimization and transfer learning approach.

ABSTRACT: Understanding human activities is one of the vital steps in visual scene recognition. Human daily activities include diverse scenes with multiple objects having complex interrelationships with each other. Representation of human activities finds application in areas such as surveillance, health care systems, entertainment, automated patient monitoring systems, and so on. Our work focuses on classifying scenes into different classes of human activities like waving hands, gardening, walking, running, etc. The dataset classes were pre-processed using the fuzzy color stacking technique. We adopted the transfer learning concept of pretrained deep CNN models. Our proposed methodology employs pretrained AlexNet, SqueezeNet, ResNet, and DenseNet for feature extraction. The adaptive World Cu

SUBMITTER: Surendran R 

PROVIDER: S-EPMC10280426 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature