Action recognition is a challenging task. Deep learning models have been investigated to solve this problem. Setting up a new neural network model is a crucial and time-consuming process. Alternatively, pre-trained convolutional neural network (CNN) models offer rapid modeling. The selection of the hyperparameters of CNNs is a challenging issue that heavily depends on user experience. The parameters of CNNs should be carefully selected to get effective results. For this purpose, the artificial bee colony (ABC) algorithm is used for tuning the parameters to get optimum results. The proposed method includes three main stages: the image preprocessing stage involves automatic cropping of the meaningful area within the images in the data set, the transfer learning stage includes experiments with six different pre-trained CNN models and the hyperparameter tuning stage using the ABC algorithm. Performance comparison of the pre-trained CNN models involving the use and nonuse of the ABC algorithm for the Stanford 40 data set is presented. The experiments show that the pre-trained CNN models with ABC are more successful than pre-trained CNN models without ABC. Additionally, to the best of our knowledge, the improved NASNet-Large CNN model with the ABC algorithm gives the best accuracy of 87.78% for the overall success rate-based performance metric.

中文翻译：

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预训练卷积神经网络的动作识别性能改进

动作识别是一项艰巨的任务。为了解决这个问题，已经研究了深度学习模型。建立新的神经网络模型是至关重要且耗时的过程。另外，预训练卷积神经网络（CNN）模型可提供快速建模。CNN的超参数的选择是一个具有挑战性的问题，在很大程度上取决于用户体验。CNN的参数应仔细选择以获得有效的结果。为此，人工蜂群（ABC）算法用于调整参数以获得最佳结果。所提出的方法包括三个主要阶段：图像预处理阶段包括自动裁剪数据集中图像中有意义的区域，转移学习阶段包括使用六个不同的预训练CNN模型进行的实验以及使用ABC算法的超参数调整阶段。提出了针对Stanford 40数据集使用和不使用ABC算法的预训练CNN模型的性能比较。实验表明，带有ABC的预训练CNN模型比没有ABC的预训练CNN模型更为成功。此外，据我们所知，使用基于ABC算法的改进的NASNet-Large CNN模型，对于基于总体成功率的性能指标而言，其最佳准确性为87.78％。实验表明，带有ABC的预训练CNN模型比没有ABC的预训练CNN模型更为成功。此外，据我们所知，使用基于ABC算法的改进的NASNet-Large CNN模型，可以为基于总体成功率的性能指标提供87.78％的最佳准确性。实验表明，带有ABC的预训练CNN模型比没有ABC的预训练CNN模型更为成功。此外，据我们所知，使用基于ABC算法的改进的NASNet-Large CNN模型，可以为基于总体成功率的性能指标提供87.78％的最佳准确性。