# Improved Video Action Recognition Based on Pyramid Pooling and Dual-Stream C3D Networks
## 1 Introduction  
Action recognition techniques aim to identify and classify different behaviours by analysing features such as human pose, movement trajectory, speed, etc, in video sequences. In recent years, deep learning, especially convolutional neural networks (CNNs), has become a mainstream approach for action recognition due to its powerful image and video-processing capabilities. CNNs are able to automatically learn feature representations from low-level to high-level, avoiding the complex process of manually designing feature extractors, and thus significantly improving recognition accuracy. As technology continues to advance, researchers have begun to introduce more complex network architectures, such as long short-term memory networks (LSTMs) and recurrent neural networks (RNNs), in order to better process time-series data and capture dynamic features of actions [1]. Despite significant progress in action recognition, deep learning methods still face many challenges, mainly including the lack of generalisation ability of the models, the high real-time requirement, and the effect of environmental disturbances. Due to the diversity and complexity of actions, the performance of existing models in different environments or datasets often varies greatly, making it difficult to effectively migrate-in new application scenarios; many practical applications (e.g., video surveillance, human-computer interaction, etc.) require the system to have a high real-time processing capability [2]; when dealing with large-scale video data, the existing methods often face speed bottlenecks; furthermore, lightingchanges, occlusion, background clutter, and other environmental factors can also affect the quality of video data, thus reducing the accuracy of action recognition.  
In order to address the limitations of existing methods, this paper proposes an improved video character classification method based on deep learning. The method combines a gated recurrent unit (GRU) with a three-dimensional convolutional neural network (3D-CNN) and an improved two-stream C3D network for more efficient behaviour classification. Specific objectives include enhancing the adaptability of the model to improve its performance in different environments and datasets through feature fusion and dynamic feature learning; optimising the real-time performance by improving the network structure to increase the processing speed and reduce the latency; and enhancing the robustness by automatically extracting features with robustness to reduce the effects of factors such as illumination changes and occlusion. The research steps include extracting static and dynamic features from video data and fusing them, designing a GRU-based video character classification method, and combining 3D-CNN with an improved C3D network for behaviour recognition. Finally, this paper conducts experiments on standard datasets such as UCF101 and HMDB51 to validate the effectiveness of the proposed method and analyses the impact of different feature fusion strategies and network structures on recognition accuracy. Through these improvements, this paper expects to improve the generalisation ability and real-time processing ability of the action recognition system while overcoming the interference of environmental factors and promoting the wide application of action recognition technology [3]. Through these improvements, this paper expects to enhance the generalisation ability and real-time processing capability of the existing action recognition system while overcoming the interference of environmental factors so as to promote the breakthrough of action recognition technology in a wider range of applications.