A Comparative Exploration of CNNs and ViTs in Deep Learning-Based Human Body Recognition
Chenghan Zou · Applied and Computational Engineering · 2025
Human body recognition is crucial for enhancing security, facilitating human-robot interaction, and improving accessibility for people with disabilities. The integration of deep learning techniques has revolutionized the field, significantly boosting the accuracy and efficiency of body recognition systems. This advancement not only improves security but also enriches the user experience in various applications, from healthcare to entertainment. This study explores the application of Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), Selective Kernel Networks (SKNs), and Adaptive Kernel Convolution (AKConv) in identifying individuals from a distance. Leveraging transfer learning from large-scale datasets like ImageNet, evaluation of these models on a standardized human body recognition dataset, focusing on the trade-off between recognition performance and computational efficiency. Findings underscore the potential of SKNs and AKConv in achieving high accuracy with reduced computational demands, paving the way for their deployment in resource-constrained environments. The research contributes to the development of more efficient recognition algorithms and provides insights for future advancements in the field.