# Sports Video Classification Method Based on Improved Deep Learning
Moreover, VGG and ResNet excel in generic image classification paradigms, they might falter in discerning the fine-tuned features intrinsic to the rapid and intricate movements characteristic of sports videos. Meanwhile, it is also noteworthy that in actual sports matches, the background is often dynamic, and the movement and speed of athletes are fast. Additionally, the broadcast or recording quality of lower-level events may not be guaranteed. Traditional deep learning-based sports video classification methods are susceptible to these factors, which could threaten their reliability. Developing strategies to mitigate these threats remains an open research area. Furthermore, the generalizability of these methods across different sports and video quality levels is limited. When faced with new domains or data distributions, they often result in significant performance drops. Techniques such as domain adaptation and data augmentation can offer assistance but come with their own set of challenges. In the field of sports video classification, recent years have seen the development of various studies and methods, especially those focusing on addressing the challenges of rapid motion and low resolution [14; 15; 16], which have provided solutions from different perspectives. However, there is still room for improvement in terms of their generalizability and accuracy. In a bid to surmount the aforementioned challenges, this paper introduces the following enhancements: (i) Bifurcated Neural Network Architecture: This design, juxtaposed against conventional CNNs, exhibits an augmented capability to extract features across diverse scales, thereby adeptly capturing the nuances of high-velocity movements. (ii) Pivotal Frame Sampling: This strategy ensures the model's focus is honed on the most salient segments of the video, striking an optimal equilibrium between precision and computational efficiency. (iii) Fuzzy Noise Attenuation Algorithm: Tailored to address the intricacies of low-resolution footage, this algorithm proficiently mitigates motion-induced artifacts and noise, bolstering the model's classification accuracy. (iv) Multiscale and Multiperspective Feature Extraction: This approach offers a holistic capture of video intricacies, ensuring rapid movements are accurately discerned, even under the constraints of diminished resolution. Specifically, the real-world key challenges and the corresponding solutions proffered in this study can be delineated as follows:  
Image Quality Degradation due to Motion Artifacts: The high-velocity movements inherent to sports videos often induce motion artifacts. These artifacts can blur the imagery, potentially misleading the feature extraction process and culminating in imprecise categorizations. To address this, our study employs the Laplacian second-order differential linear operator to discern high-frequency components within the input imagery. A scarcity of high-frequency components suggests potential blurring and motion artifacts. Concurrently, assuming the original image has been displaced by L pixels either vertically or horizontally, a Fourier transform is invoked to decompose and rectify this blurring. The Radon transform is leveraged to compute the frequency offset angle, yielding the blur kernel angle. An inverse Fourier transform is then executed to retrieve a pristine image. The Laplacian operator meticulously identifies frames afflicted by blurring, and the Fourier transform technique rectifies these artifacts, ensuring feature extraction transpires on artifact-free, sharp imagery. Performance Issues of Traditional Feature Extraction Techniques in Low Resolution: Sports videos, often captured under varied conditions, exhibit inconsistent resolutions, occasionally skewing towards the lower end. While conventional neural network architectures flourish on high-resolution imagery, their efficacy wanes on low-resolution footage.