# Shot Classification of Field Sports Videos Using AlexNet Convolutional Neural Network
## 1 Introduction  
The last few decades have witnessed the production and transmission of a massive amount of multimedia content in the World Wide Web. Sports videos contribute a major portion of the available multimedia content in cyberspace. Sports video content analysis has been explored heavily due to the potential commercial benefits and massive viewership around the world. The manual annotation of such an enormous video content is a challenging task. Therefore, automated methods for sports video content analysis and management are required. Humans are inclined towards meaningful concepts, while viewing and exploring interactive databases; there is a growing need for indexing and semantic video content analysis. The major goal towards video content analysis and management is to provide semantic information relevant to the user. For this purpose, researchers have proposedvarious automated and semi-automated techniques to analyze the sports videos for shot classification, key-events detection, and video summarization. Shot Classification approaches are commonly applied initially for sports video summarization and content retrieval applications to provide the semantic information. Shot Classification techniques are employed to categorize the sports videos into different views, i.e., long, medium, close-up, etc. The classification makes it convenient to further process the sports videos for various applications mentioned above. The effectiveness of these applications largely depends on accurate shot classification of the sports videos. However, shot classification is very challenging in the presence of camera variations, scene change, action speeds, illumination conditions (i.e., daylight, artificial light, and shadow), etc. Therefore, there exists a need to develop an effective shot classification method that is robust to the above-mentioned limitations. The main idea is to develop a shot classification scheme that can accurately classify the frames of field sports videos in the presence of the aforementioned limitations. Soccer and Cricket field sports are selected to evaluate the effectiveness of our framework due to the massive viewership of these sports across the globe. Sports videos contain different types of shots that can be categorized into following categories: (i) Long shot, (ii) medium shot, (iii) close-up shot, and (iv) crowd/out-of-the-field shots. Long shots in sports videos are considered zoomed-out shots that covers a large area or a landscape of the field, the audience and the players are presented on a small scale. Medium shots are zoomed-in shots that mostly cover a full body of the player from top head of the body to the bottom of the feet or more than one player within the playground field. Close-up shots are the most zoomed-in shots that contain the players upper body footage in the scene. Crowd shots/out-of-the-field shots consist of viewers/audience footage in the sports videos. Most of the existing methods classify the in-field segments of sports videos, i.e., long, medium, and close-up shots as these shots are more useful to generate the highlights. After viewing massive number of sports videos, it has been observed that crowd/out-of-the-field shots also contain few exciting events that can be included in the summarized video. Therefore, we proposed an automated shot classification method of sports videos into long, medium, close-up and crowd/out-of-the-field shots. Shot classification methods have applications in various domains, i.e., sports [1], medical [2], news [3], entertainment [4], and documentaries [5]. Existing approaches of shot classification can be categorized into (i) learning-based, and (ii) non-learning-based. Learning-based approaches employ various classifiers (i.e., Support Vector Machines (SVM), Kth Nearest Neighbor (KNN), decision trees, neural networks, etc.) to perform shot classification.