Complex interaction recognition <i>via</i> advanced multilevel feature fusion and deep learning model

The recognition of human activities through images represents a fundamental research domain in computer vision and pattern recognition, with practical applications in human–computer interaction, video analysis, and surveillance. This research objective is to develop an enhanced neurocomputing network for humans using Deep Neural Networks (DNNs) for precise image-based behaviour classification. Our proposed DNN-based framework unites several preprocessing and feature extraction methods to accomplish this goal. The system begins with Hue, Saturation, and Value (HSV) colour processing to enhance image visibility, followed by Gaussian filtering for noise reduction. The statistical method performs silhouette extraction, and the feature extraction process utilises Local Intensity Order Pattern (LIOP) and Features from accelerated segment test (FAST) algorithms together. Feature discrimination is enhanced through the application of fuzzy optimisation techniques. The optimised features are processed by a DNN, which classifies human activities. The proposed framework demonstrates high effectiveness through its recognition performance, which achieved 94% accuracy on BIT interaction data and 88.25% accuracy on SBU interaction data. This work presents an advanced human activity recognition system that shows promise for real-world applications, such as surveillance systems, video analytics, and interactive technologies, enabling more precise analysis of human behaviour.