Semantic Analysis in Soccer Videos Using Support Vector Machine


Elgamml, Mohamed M. and Abas, Fazly Salleh and Goh, Hock Ann (2020) Semantic Analysis in Soccer Videos Using Support Vector Machine. International Journal of Pattern Recognition and Artificial Intelligence, 34 (9). p. 1. ISSN 0218-0014

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A tremendous increase in the video content uploaded on the internet has made it necessary for auto-recognition of videos in order to analyze, moderate or categorize certain content that can be accessed easily later on. Video analysis requires the study of proficient methodologies at the semantic level in order to address the issues such as occlusions, changes in illumination, noise, etc. This paper is aimed at the analysis of the soccer videos and semantic processing as an application in the video semantic analysis field. This study proposes a framework for automatically generating and annotating the highlights from a soccer video. The proposed framework identifies the interesting clips containing possible scenes of interest, such as goals, penalty kicks, etc. by parsing and processing the audio/video components. The framework analyzes, separates and annotates the individual scenes inside the video clips and saves using kernel support vector machine. The results show that semantic analysis of videos using kernel support vector machines is a reliable method to separate and annotate events of interest in a soccer game.

Item Type: Article
Uncontrolled Keywords: Semantic computing
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75-76.95 Calculating machines
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 19 Oct 2020 21:51
Last Modified: 20 Oct 2020 15:45


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