# Improved Video Action Recognition Based on Pyramid Pooling and Dual-Stream C3D Networks
## 2 Methodology
### Classification of Movements
#### 2.4.2 Video Character Behaviour Classification Network Based on Dual-Stream Improved C3D  
Although the traditional C3D network is capable of extracting spatio-temporal features of the video, its extraction of temporal features is rougher, and thus it is inferior to the dual-stream method [23] in terms of recognition accuracy. In the video action recognition task, spatial and temporal streams are two key components that are responsible for extracting different types of information from the video to ensure that the video content can be fully understood. The spatial stream is mainly responsible for extracting static spatial features from video frames, and by processing each frame, the spatial stream is able to capture structural features in the image, such as the pose of a person, the shape of an object, and the static parts of the background. Since each frame in a video is essentially a still image, spatial streaming focuses on extracting the static information of these images, such as edges, textures, colours, and so on. In this way, spatial streaming helps to capture local details in the video and build an image-level spatial representation; temporal streaming focuses on extracting the dynamic information in the video. Unlike spatial streaming, which deals with static images, temporal streaming deals with the temporal dimension of the video and captures the dynamic behaviour in the video mainly by analysing the motion and changes between consecutive frames [24]. By introducing optical flow features, temporal flow is able to effectively capture the trajectory of objects and the movement process of characters, thus identifying the actions and activities in the video. These dynamic features are essential for recognising rapid changes and continuous actions, so temporal flow often complements spatial flow information by extracting motion patterns in the video. Combining the dual-stream method and the C3D network and using the C3D network to extract RGB features and optical flow features, respectively, can effectively improve the extraction of spatio-temporal features.  
Figure 6 illustrates the improved two-stream C3D network architecture. The network is divided into two streams, the RGB stream for extracting spatial features and the optical stream for extracting temporal features [25]. The dual-stream C3D network has two main architectural improvements: 1. the maximum pooling layer is replaced with a 3D pyramid pooling layer, and 2. a fully-connected layer is added based on the number of action categories to enhance the representation of action features. In dual-stream networks (e.g., dual-stream C3D networks), spatial and temporal streams are usually combined by early or late fusion. The spatial stream provides the static spatial characteristics of the video, while  
the temporal stream complements the dynamic information of the video. By combining these two types of information, the model is able to understand both the static structure and the dynamic changes in the video, thus improving the accuracy of action recognition. In the improved approach of this paper, an early fusion strategy is used, which fuses the features of spatial and temporal streams at an early stage of the network to ensure that both complement each other at a preliminary stage and provide a more comprehensive representation of the features [26]. This fusion approach is able to better preserve the features of each stream compared to the traditional late fusion approach, thus improving the accuracy of the recognition.