Event-based sports videos classification using HMM framework

S, Wilson and C, Krishna Mohan and Kodukula, Sri Rama Murty (2014) Event-based sports videos classification using HMM framework. Advances in Computer Vision and Pattern Recognition, 71. pp. 229-244. ISSN 2191-6586

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Sports video classification is an important application of video content analysis. Event detection or recognition in sports video is an important task in semantic understanding of video content. In this paper, we propose a framework based on hidden Markov models (HMM) to represent a video as a sequence of core events that occur in a particular sport. The objective is to observe a subset of (hidden) state sequences to see whether they can be interpreted as the core events of that sport. We propose a method for sports video classification based on the events in each sport category. The proposed method for detection of events is based on a subset of state sequences, unlike the traditional way of computing the likelihood as a sum or maximum overall possible state sequences. The neighboring frames are considered for the computation of event probability at any instant of time. We demonstrate the application of this framework to five sport genre types, namely basketball, cricket, football, tennis, and volleyball.

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IITH Creators:
IITH CreatorsORCiD
Kodukula, Sri Rama Murtyhttps://orcid.org/0000-0002-6355-5287
Item Type: Article
Subjects: Computer science > Big Data Analytics
Others > Electricity
Divisions: Department of Electrical Engineering
Depositing User: Team Library
Date Deposited: 18 Feb 2015 11:32
Last Modified: 05 Dec 2017 03:59
URI: http://raiith.iith.ac.in/id/eprint/1346
Publisher URL: https://doi.org/10.1007/978-3-319-09396-3_11
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