Group activity analysis holds diverse applications across various domains, encompassing daily behavior scrutiny, sports event investigation, and social behavior analysis. What sets the methodology proposed in this paper apart in group behavior analysis is its dual focus: not only does it scrutinize the behavioral trends exhibited by the collective but also delves into the nuanced variations in individual behaviors. While comprehensively examining group behavior, this approach offers researchers a fresh lens to discern individual behavioral traits, crucial for delving into and elucidating the intricacies of group behavior comprehensively. To validate the efficacy of this novel approach, we meticulously conducted experiments using the volleyball dataset and devised a corresponding system framework. The experimental outcomes yielded satisfactory results, showcasing the potential of our model in analyzing both group behavior and individual disparities, thus paving a novel pathway for practical applications.
Due to the new crown and other epidemic diseases that make people wear masks to travel, the accuracy of the original face recognition system is affected. To address this challenge, a mask-wearing face recognition system based on an improved attention mechanism is proposed. First, Adding a maximum pooling operation to the CA (Coordinate Attention) attention module, then, placing attention module in the residual unit to form a feature extraction network. LResNet18E-IR is selected as the backbone network. Finally, the ArcFace loss and occlusion probability loss are combined to establish a multi-task network, which further promotes the accuracy of occluded face recognition. The results demonstrate that the system effectively increases the recognition accuracy of masked face and maintains almost the same accuracy as the original model on the unmasked dataset.
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