# Sports Video Classification Method Based on Improved Deep Learning  
[PERSON]  
1 Department of Sport Science, College of Education, Zhejiang University, Hangzhou 310027, China; [EMAIL_ADDRESS] (T.G. ); [EMAIL_ADDRESS] (M.Z. ); 1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 1  
video attributes accentuates these limitations. To elucidate: (i) Temporal Intensity: The crafting of such features demands an extensive reservoir of domain expertise and timely investment. This translates to potentially weeks or even months dedicated to the design and fine-tuning of these features for each novel task or domain. For instance, architecting a feature tailored for football match analysis necessitates profound insights into the sport's intricacies and iterative experimentation. (ii) Task Specificity: Given the bespoke nature of these features, tailored for distinct tasks or domains, their efficacy may wane when transposed to alternative contexts. Drawing from the realm of sports, a descriptor sculpted for baseball might falter when applied to basketball or football, given the stark divergence in rules and foundational actions. (iii) Challenges of High-Velocity Movements: Actions that transpire at elevated speeds might span merely a few frames within a video, yet they encapsulate a wealth of information. Manually crafted features might falter in capturing these ephemeral yet pivotal movements, culminating in the omission of crucial data. For instance, in table tennis, a game-altering rapid spin might elude capture through traditional handcraft techniques. (iv) Pitfalls with Low-Resolution Footage: The detail attrition inherent to low-resolution videos presents another formidable challenge for handcrafted features. For instance, when revisiting and analyzing archival footage from seminal events, constrained by the technological limitations of yesteryear recording apparatuses, one grapples with the intricacies of processing and recognizing low-resolution video samples. Herein, handcrafted features often stumble, leading to detail erosion. Such challenges can culminate in harmful outcomes, like skewed system predictions and resource misallocation. Take football match analysis as an example: if a researcher manually crafts a feature to monitor player movements, the confluence of rapid player motion during pivotal goal opportunities and low video resolution might compromise the feature's ability to accurately pinpoint player positions and actions. This could relay misleading tactical insights to the coaching staff, potentially swaying the match's trajectory. In essence, while manually engineered descriptors might exhibit efficacy under certain circumstances, their inherent limitations and potential pitfalls have catalyzed a shift towards deep learning-based automatic feature extraction techniques, renowned for their robustness amidst intricate and dynamic data. Consequently, the academic background has witnessed an augmented inclination towards unearthing methodologies that can surmount these impediments. Deep learning, with an emphasis on Convolutional Neural Networks (CNNs), has burgeoned as a formidable contender in the realm of sports video categorization, showcasing exemplary prowess in image classification endeavors. CNNs harbor the potential for fine-tuned adaptation to video datasets, facilitating the discernment of distinct actions and dynamics inherent to diverse sporting disciplines. Although traditional CNN architectures, such as VGG [12] and ResNet [13], have garnered significant accolades across a plethora of visual tasks, they encounter specific challenges when navigating sports videos replete with high-velocity movements. Firstly, conventional CNNs, having been trained on high-resolution image datasets, might witness performance degradation when confronted with low-resolution or suboptimal quality sports videos. The absence of efficacious preprocessing techniques to mitigate these artifacts further jeopardizes classification accuracy.