# Shot Classification of Field Sports Videos Using AlexNet Convolutional Neural Network
## 4 Conclusions and Future Work  
We proposed an AlexNet convolutional neural network-based model for shot classification of field sports videos. Our framework is robust to camera variations, scene change, action speeds, and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Shot Classification** & **Technique** & **Dataset** & **Shots Type** & **Recall** & **Precision** \\ \hline [PERSON] et al. (2018) & SVM, KNN Classifiers & Soccer Videos & Long, medium, close, out-field & 89.93 & 90.3 \\ [PERSON] et al. (2018) & SVM & Soccer & Long, medium, close, out-field & 93.85 & 91.07 \\ [PERSON] et al. (2018) & SVM Classifier & Field Sports & Close-up, Long & 84.2 & 82.5 \\ [PERSON] et al. (2018) & PFF-Net & Soccer Videos & Close, medium, long, out-of-the-field & 91.3 & 90.6 \\ [PERSON] et al. (2018) & Deep net (CC-Net) & Basketball videos & \
\begin{tabular}{c} Close, medium close, medium, \\ medium long, long, extreme \\ long \\ \end{tabular} &
\begin{tabular}{c} 90 \\ \end{tabular} \\ [PERSON] et al. (2018) & Bayesian Based & Soccer videos & Long, Close-up & 84 & 86 \\  & Network (BB-Net) & & & & \\ [PERSON] et al. (2018) & Rule-based & Soccer Videos & Long & 80 & 86 \\  & Thresholding & & & & \\ Proposed System & AlexNet CNN & Cricket \& Soccer & Close, medium, long, crowd/out-field & 96.24 & 94.07 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Performance comparison with existing shot classification methods.  
illumination conditions (i.e., daylight, artificial light, shadow). The proposed framework successfully classifies the input sports video into long, medium, close-up, and crowd/out-field shots in the presence of these limitations. Experimental results signify the effectiveness of our framework in terms of shot classification of field sports videos. We compared the performance of the proposed method with existing state-of-the-art methods. In addition, we specifically designed an experiment to extract LBP and LTrP features and trained them on SVM and ELM separately for shot classification. We also evaluated the performance of shot classification using the KNN and standard CNN. Afterwards, we compared the results obtained on SVM, K-NNELM and standard CNN with the proposed method. The comparison clearly shows that the proposed framework provides better classification performance, as compared to SVM ELM, K-NN, and standard CNN classifiers. It is to be noted that the use of CUDA Graphics Processing Unit namely GTX 580 can further increase the processing speed of the proposed method. Optimization level is achieved by just changing CUDA 480 to CUDA 580 GB graphics card.  
Currently we are investigating the performance of the proposed method on a more diverse and larger dataset. We would preferably be working on small data that is able to define some training and validation percentage of data required to make a trade-off between efficient and effective classification phenomena. Moreover, it would be interesting to investigate the performance of various classifiers for the proposed features and the undertaken problem. Particularly, in future work, the performance of the resultant classifiers from the combination of weak classifiers such as random forest will be analyzed. In addition, these excellent results of shot classification can be further used to increase the accuracy of video summarization systems.  
Data curation, J.B.J.; Funding acquisition, M.T.M.; Investigation, A.I.; Methodology, A.I.; Project administration, J.B.J.; Resources, M.T.M.; Software, R.A.M. and A.J.; Validation, A.J.; Writing--original draft, R.A.M.; Writing-review & editing, A.J., A.I., M.T.M. and J.B.J.  
This research work was supported by Korea University of Technology and Education (KOREATECH), Education and Research Promotion Program (2017).  
The authors have no conflict of interest.