# Shot Classification of Field Sports Videos Using AlexNet Convolutional Neural Network
## 1 Introduction
[19] proposed a multi labelled video dataset that contained over eight million videos, 500k hours of video, annotated with 4800 visual entities. The videos were labelled using Youtube vide annotation system. Each video was decoded using Deep CNN pre-trained on ImageNet to extract hidden representation immediately. Training was performed on different classification models on the dataset. The videos were classified onto different categories like vehicles, sports, concert, and animated, etc. [PERSON] et al. [20] proposed a video event classification algorithm using audio-visual features. Convolutional neural networks were applied on the frames to extract the features followed by performing the classification. In addition, Mel Frequency Cepstral Coefficients (MFCCs) were also used to train the CNN for shot classification. [PERSON] et al. [21] proposed a semantic shot classification algorithm for cinematography. Markov random field model based on motion segmentation was used to classify the film video into three types of shots, i.e., long, medium, and close-up. [PERSON] et al. [22], proposed a hybrid classifier-based approach for activity detection of cricket videos. Low-level features (i.e., grass pixel ratio) and mid-level features (i.e., camera view, distance, etc.) were used to train a hybrid classifier comprising of Naive bias, KNN and multi-class SVM for shot classification into field-view and non-field views. As we already discussed that effective shot classification improves the accuracy of video content analysis applications. However, shot classification is very challenging in the presence of camera variations, scene change, action speeds, illumination conditions (i.e., daylight, artificial light, shadow), etc. To address the challenges associated with shot classification, we proposed an effective shot classification framework for field sports videos. The major contributions of the proposed research work are as follows:  
* AlexNet convolution neural network is designed to effectively classify the video shots into different views (i.e., long, medium, close-up, crowd/out-of-the-field) which is promising and novel in terms of its application to shot classification. * The proposed framework is robust to camera variations, scene change, action speeds, and illumination conditions and can be reliably used for shot classification in the presence of these limitations. Moreover, existing shot classification approaches focus on generating more classes for in-field segments (long, medium and close-up shots) because in-field segments are commonly used to generate the sports highlights. However, it has been observed after watching many sports videos that the crowd shots also contain few exciting segments that can be included in the summarized video. Therefore, the proposed research work focuses on classifying the shots of field sports videos into long, medium, close-up, and crowd/out-of-the-field shots. We categorize the crowd shots into separate class so that these shots can also be analyzed to further identify the interesting segments for video summarization. These crowd shots can also be used for different applications, i.e., activity detection, etc. The details of the proposed deep learning framework are provided in the next section.