Human action recognition in videos is a critical task with significant implications for numerous applications, including surveillance, sports analytics, and healthcare. The challenge lies in creating models that are both precise in their recognition capabilities and efficient enough for practical use. This study conducts an in-depth analysis of various deep learning models to address this challenge. Utilizing a subset of the UCF101 Videos dataset, we focus on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Two-Stream ConvNets. The research reveals that while CNNs effectively capture spatial features and RNNs encode temporal sequences, Two-Stream ConvNets exhibit superior performance by integrating spatial and temporal dimensions. These insights are distilled from the evaluation metrics of accuracy, precision, recall, and F1-score. The results of this study underscore the potential of composite models in achieving robust human action recognition and suggest avenues for future research in optimizing these models for real-world deployment.

翻译：视频中的人体动作识别是一项关键任务，对监控、体育分析和医疗保健等众多应用具有重大意义。挑战在于构建既具备精确识别能力又足够高效以适应实际应用的模型。本研究针对这一挑战，对多种深度学习模型进行了深入分析。利用UCF101视频数据集的一个子集，我们重点研究了卷积神经网络（CNN）、循环神经网络（RNN）以及双流卷积网络（Two-Stream ConvNets）。研究揭示，虽然CNN能有效捕获空间特征，RNN能编码时序序列，但双流卷积网络通过整合空间与时间维度展现出更优性能。这些结论基于准确率、精确率、召回率和F1分数等评估指标。本研究结果强调了复合模型在实现稳健人体动作识别方面的潜力，并为优化这些模型以应用于实际场景的未来研究指明了方向。