# Shot Classification of Field Sports Videos Using AlexNet Convolutional Neural Network
## 3 Results and Discussion
### Performance Comparison with Existing Methods  
In the last experiment, we compared the performance of our method against the existing shot classification methods [24; 25; 26] and [30; 31; 32; 33] for sports videos. [PERSON] et al. [24] proposed a Bayesian network-based model for event detection and video summarization in soccer videos. Markov model and hue histogram differences were used to detect the shot boundaries. These shots were classified as long view, medium view, and close-up view depending upon the size of the players and dominant color. Bayesian networks were applied to classify the events, i.e., goal, foul, etc. [PERSON] et al. [25] proposed a shot classification method for soccer videos. First, the in-field video frames were separated from the out-field frames. In the next stage, three features (i.e., number of connected components, shirt color in vertical and horizontal strips) were extracted from the in-field frames and fed to SVM to classify the long, medium, and close-up shots. [PERSON] et al. [26] used radial basis decomposition function (RBF) and Gabor wavelets to propose a method for scene classification of sports videos. The input image was transformed into HSV color space followed by applying the Fourier transform on the image. Gabor filters were applied and trained the SVM to classify the scene into different shots. [PERSON] et al. [30] proposed a deep learning fused features-based framework to classify the shot types using the camera zoom and out-field information. The soft-max and fussing Bayesian layers were used to classify the shots into long, medium, close-up, and out-field shots. [PERSON] et al. [31] proposed a system for automatic segmentation of basketball videos based on GOP (group of pictures). Long view, medium view and full court view were classified using the dominant color feature and length of the video clips. [PERSON] et al. [32] proposed a system that generated highlights from the soccer videos. Bayesian network was employed to classify the video into replay, player, referee, spectator and playing gathering shots based on the audio features. Exciting segments from the soccer videos were detected that are assigned semantic concept labels like goals, save, yellow-cards, and kicks in sequence. Classification accuracy for the exciting segments was observed to be 86%. [PERSON] et al. [33] proposed a video summarization method for soccer based on audio-visual features. Shot segmentation was performed initially to select the frames for video summarization based on the relevance. Rule-based thresholding was applied on the grass field color pixels to detect the long shots in soccer videos. The average accuracy of the proposed and existing shot classification approaches is provided in Table 11.  
From the results shown in Table 11, we can clearly observe that the proposed method was evaluated over a diverse dataset of sports videos; and achieved the highest precision and recall values, as compared to the existing state-of-the-art shot classification methods. Although the videos used in our method and comparative methods are different but the experimental setup of our method and comparative methods is similar in terms of video source and content selection. We selected the videos from YouTube as done by the comparative methods and the selected videos represent  
Figure 8: Performance comparison with different classifiers. different broadcasters, different genres, and different tournaments. The videos also represent different illumination conditions (i.e., all-day videos, day, and night videos, and night videos recorded in electric lights), and various camera shot types (i.e., long, medium, close-up and crowd/out-field shots) as selected by the comparative approaches. By testing our method on a diverse set of sports videos captured under the challenging conditions for shot-classification we ensured the fair comparison against the state-of-the-art methods.