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Human activity recognition (HAR) is a focal point of study in the realms of human perception and computer vision due to its widespread applicability in various contexts, such as intelligent video surveillance, ambient assisted living, HCI, HRI, IR, entertainment, and intelligent driving.With the prevalence of deep learning techniques for image classification, researchers have shifted away from the labor-intensive practice of hand-crafting in favor of these methods in HAR.However, Convolutional Neural Networks (CNNs) face challenges such as the receptive field problem and limited sample issues that remain unsolved.This paper introduces a two-branch convolutional neural network for HAR classification, incorporating a polarized full attention method to address the aforementioned issues.The Artificial Butterfly Optimization (ABO) is employed for optimal hyper-parameter tuning.The proposed network utilizes twobranch CNNs to efficiently extract data, simplifying convolutional layers' kernel sizes to enhance network training and suitability for low-data settings.Feature extraction effectiveness is improved by implementing the one-shot assembly method.To amalgamate feature maps and provide global context, an enhanced full attention block called polarized full attention is utilized.Experimental results demonstrate the superiority of the proposed model in detecting human behaviors on the LoDVP Abnormal Behaviors dataset and the UCF50 dataset.Furthermore, the suggested model is adaptable to incorporate new sensor data, making it particularly valuable for real-time human activity identification applications.The Recall is 100 for the 1st dataset, 94 for the 2nd dataset, and 100 for the 3rd dataset, respectively.The F1-Score is 96.61836 for the 1st dataset, 96.90722 for the 2nd dataset, and 98.03922 for the 3rd dataset, respectively.
Jagadesh et al. (Thu,) studied this question.