# Sports Video Classification Method Based on Improved Deep Learning
## 2 Methods
### Model and Training  
To extract the initial 80 frames from each video and mitigate potential noise from irrelevant backgrounds, we employ center cropping on these frames. The shorter dimension of the image dictates the side length of the central square, which is subsequently resized to 299 \(\times\) 299 pixels via the OpenCV library. Consequently, each sports video input manifests as an 80 \(\times\) 299 \(\times\) 299 \(\times\) 3 tensor. In the first branch, the process begins with fine-grained feature extraction from the downsampled image, leading to the generation of Feature 1. This extraction, as detailed in Figure 3, starts by dividing the image into four blocks. Each block undergoes a specialized 1 \(\times\) 1 convolution process, utilizing 32 filters. These filters are complemented by a ReLU activation function and a stride of 1, optimizing the extraction process. The output from each block is a feature map, which are then concatenated along the spatial dimension. This concatenated map undergoes a further transformation through a 1 \(\times\) 1 convolution with 128 filters, same padding, and a stride of 1, effectively restoring the dimensions of the combined feature map. This restored map is then processed through two sequential Conv2D layers, each employing two 3 \(\times\) 3 convolution kernels with a stride of 1 and 32 filters. These layers are followed by a 2D average pooling, a batch normalization layer to expedite convergence, and a Mish activation function layer, further refining the feature extraction. The process concludes with a downsampling layer, producing a refined feature map designated as f1. The second branch initiates with F0 undergoing feature extraction through two Conv2D layers, followed by a downsampling step to obtain feature map F2, as detailed in Figure 4. Subsequently, the network pathway behind F2 splits into a main branch and an auxiliary branch. In the \(-\)main branch, F2 is processed through a fine-grained feature layer, transforming it into F2\({}^{\prime}\). This new feature map, F2\({}^{\prime}\), is then concatenated with F1 in the channel dimension and further processed through a Conv2D layer, resulting in F2\({}^{\prime\prime}\). Concurrently, in the auxiliary branch, F2 first passes through a Conv2D layer, followed by a downsampling layer. After skip concatenation with F1, it undergoes another round of fine-grained feature extraction. This is followed by an upsampling layer employing bilinear interpolation, which yields feature F3. Finally, F3 is concatenated with F2\({}^{\prime\prime}\), culminating in the final feature, F_final. This intricate process, where features from different convolutional depths are fused, encapsulates a richer array of local and global information. This comprehensive fusion significantly enhances the model's capability to understand and interpret images, leveraging the unique strengths of each convolutional layer and processing step. Figure 4: Two\(-\)branch Neural network model architecture for sports video classification. Figure 3: Fine\(-\)grained feature extraction architecture. The specific classification mechanism is as follows:  
The latter segment of our methodology zeroes in on feature classification. Leveraging the backbone network, we extract pivotal features from 80 curated frames per video. Given the multi-dimensional character of these features, a Flatten layer is employed to reshape them into a linear format, ensuring seamless interfacing with the ensuing network layers and facilitating efficient data flow. For the classification module, we harness the prowess of an advanced LSTM network adept at serializing image frames. Videos, by nature, are sequential, demanding a distinct analytical approach compared to standalone images. LSTMs, with their capacity to store and process extended sequences, are paramount for discerning the temporal intricacies inherent to video data. To bolster the LSTM's sequential comprehension, position encoding is integrated into each frame.