# Shot Classification of Field Sports Videos Using AlexNet Convolutional Neural Network
## 1 Introduction
Whereas, non-learning-based approaches use various statistical features, i.e., dominant grass color, histogram difference comparison, etc., to design different shot classification methods. Existing methods [6; 7; 8; 9; 10] have used non-learning-based approaches for shot classification of sports videos. [PERSON] et al. [6] proposed an effective shot classification method for basketball videos. Edge pixel ratio was used to classify the shots into two categories only: (i) Close-up view, and (ii) long view. Logo transition detection and logo sample selection were used to identify the replays. [PERSON] et al. [7] proposed a method to assign the semantic information to cricket videos. The input video was segmented into shots using the scene information extracted from the text commentary. Video shots were classified into different categories, i.e., batsman action of pull shot, etc. [PERSON] et al. [8] proposed a shot classification method for soccer videos using the Bhattacharyya distance [9]. [PERSON] et al. [10] proposed a soccer video summarization method using cinematic and objects-based features. The dominant field color was used to detect the soccer field. Grass pixel ratio and color histogram comparison were used to detect the shot boundaries. Finally, statistical analysis was used to classify the shots into long, medium, and close-up shots. In literature, approaches [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22] also use learning-based techniques for shot classification. Learning-based approaches provide better accuracy as compared to non-learning-based approaches, but at the expense of increased computational cost. [PERSON] et al. [11] presented an automated approach to identify the types of views and detection of the significant events such as goal, foul, etc, using bag of words and SVM. [PERSON] et al. [12] used rule-based heuristics and SVM to classify the shots into far view/long shot, medium view, close-up view, and out-field view. [PERSON] et al. [13] proposed a framework for semantic shot classification in sports videos. Low-level features were derived using color, texture, and DC images in compressed domain. These low-level features were converted to mid-level features such as camera motion, action regions and field shape properties, etc. Mid-level representation was necessary to use the classifiers that do not map on low-level features. Finally, shots were classified using different supervised learning algorithms like decision trees, neural networks, support vectors machine (SVM), etc. Camera shots were classified into mid shots, long shots, and close-up shots. [PERSON] et al. [14] proposed and evaluated deep neural network architectures that were able to combine image information across a video over longer time periods. They [14] have proposed two methods for handling full length videos. Results were evaluated over previously published datasets like Sports 1 million dataset, UCF-101 Dataset. The first method explored convolutional neural networks that was used to examine the design choices needed while adapting to this framework. The second method used an ordered sequence of frames that were employed to recurrent neural network based on Long Short-Term Memory (LSTM) cells connected to the output of underlying CNN. [PERSON] et al. [15] proposed a cricket shot classification approach using motion vector detection through the optical flow method. 3D MACH filter was used for action recognition that was trained over six cricket videos to classify the different shots. [PERSON] et al. [16] proposed a learning-based method that used feed-forward neural networks, ELMAN neural network, and decision trees to classify the different events in field sports videos. [PERSON] et al. [17] presented a shot classification model using linear discriminant analysis (LDA) method to categorize the shots into full long view and player medium view. [PERSON] et al. [18] proposed a shot classification approach using statistical features to classify the crowd shots. [PERSON] et al.