Shot Classification of Field Sports Videos Using AlexNet Convolutional Neural Network

• Published in: Applied sciences 2019-01, Vol.9 (3), p.483
• Main Authors: Minhas, Rabia A., Javed, Ali, Irtaza, Aun, Mahmood, Muhammad Tariq, Joo, Young Bok
• Format: Article
• Language: English
• Subjects: AlexNet CNN; Automation; Cameras; Classification; Computer & video games; Computer science; Content analysis; Convolution; convolutional neural networks; deep learning; Engineering; Feature maps; Image retrieval; Information processing; International conferences; Internet; Learning algorithms; Methods; Model accuracy; Multimedia; Neural networks; rectified linear unit layer; Semantics; Shot; shot classification; Soccer; Sports; Support vector machines; Video data
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app9030483

Broadcasters produce enormous numbers of sport videos in cyberspace due to massive viewership and commercial benefits. Manual processing of such content for selecting the important game segments is a laborious activity; therefore, automatic video content analysis techniques are required to effectively handle the huge sports video repositories. The sports video content analysis techniques consider the shot classification as a fundamental step to enhance the probability of achieving better accuracy for various important tasks, i.e., video summarization, key-events selection, and to suppress the misclassification rates. Therefore, in this research work, we propose an effective shot classification method based on AlexNet Convolutional Neural Networks (AlexNet CNN) for field sports videos. The proposed method has an eight-layered network that consists of five convolutional layers and three fully connected layers to classify the shots into long, medium, close-up, and out-of-the-field shots. Through the response normalization and the dropout layers on the feature maps we boosted the overall training and validation performance evaluated over a diverse dataset of cricket and soccer videos. In comparison to Support Vector Machine (SVM), Extreme Learning Machine (ELM), K-Nearest Neighbors (KNN), and standard Convolution Neural Network (CNN), our model achieves the maximum accuracy of 94.07%. Performance comparison against baseline state-of-the-art shot classification approaches are also conducted to prove the superiority of the proposed approach.