# Sports Video Classification Method Based on Improved Deep Learning
The detail attrition and pixel loss in low-resolution videos impede traditional techniques from accurately capturing pivotal visual features, leading to performance degradation. In response, we introduce a novel bifurcated neural network architecture capable of gleaning granular local features across diverse scales and fields of view. This ensures that even under diminished resolution, pivotal features are captured for precise classification. By synergistically harnessing multi-scale and multi-view feature extraction, our methodology retains efficiency and precision on low-resolution videos, transcending the constraints of traditional approaches. The proposed dual-branch neural network efficiently fuses diverse and complementary local features, enabling improved classification performance in challenging scenarios, such as high-speed sports and low-resolution videos. We present a comprehensive evaluation of the method using the self-collected High-Definition Sports Video Dataset (DeepSports-VDS), and sports videos with motion artifacts and a lower resolution as an additional validation dataset. Our key contributions are as follows: (i) Problem-driven Approach: We introduced a novel approach that specifically addresses the challenges of classifying sports videos, especially when dealing with high-speed movements and low-resolution inputs. (ii) Noise and Motion Artifact Reduction: Leveraging the Laplacian second-order differential linear operator, we effectively identified and removed motion artifacts from sports videos, thereby enhancing the clarity of images, especially in high-speed sports scenarios. (iii) Multiscale and Multiperspective Feature Extraction: Our unique dual-branch neural network architecture facilitates the extraction of fine-grained local features across multiple scales and fields of view. This ensures the capture of essential features even in low-resolution scenarios, leading to more accurate classifications. (iv) Better Performance across Different Resolutions: On the High-Definition Sports Video Dataset (DeepSports-VDS), our method achieved an unparalleled accuracy of 0.9804, surpassing the ResNet-50, by a substantial 1%. Similarly, on the Custom Low-Resolution Dataset, our technique demonstrated an accuracy of 0.8803, outpacing the closest competitor, Densenet, by approximately 0.5%. In conclusion, this study aims to address the limitations of traditional sports video classification methods by proposing an improved deep learning model. Our research first introduces a method for selecting important video frames and applies a fuzzy noise attenuation algorithm to enhance video quality. Then, we use a dual-branch neural network to extract detailed features and classify videos through a densely connected neural network with specific activation functions. Our model was tested on a dataset of high-definition sports videos covering over 20 sports and a low-resolution dataset. This study emphasizes a robust approach to sports video classification, making key contributions to improving the processing capability for high-speed actions and low-resolution inputs, and ensuring superior performance at different resolutions. Our results pave the way for advancements in the field of sports video analysis and are significant for future applications in sports data analysis, content organization, and retrieval processes.