International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064

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Survey Paper | Computer Science & Engineering | India | Volume 3 Issue 12, December 2014

Implementing Dynamic Bayesian Network for Soccer Videos Event Detection and Summarization

Rupali S. Bongane | Sachin S. Bere

Abstract: There are expansive number of games features are accessible over the Internet. Anyhow utilizing these videos to get data is a difficult and time intensive task. In this way, extraction on occasions from the videos is needed. Semantic analysis of videos and automated event extraction assumes a key part in a few applications; utilizing content-based web search engines, indexing of video, and summarization of video. As an influential way for learning complex patterns is the Bayesian Network, this paper proposes a novel Bayesian Network (BN) based strategy automatic event recognition and summarization in Soccer videos. This system incorporates proficient shot view classification, shot boundary detection algorithms, and the related Bayesian network development. There are three fundamental stages: First is detection of the shot boundaries. The video is segmented into large and meaningful semantic segment known as play-break sequences by utilizing hidden Markov model. In second stage, play-break sequences are utilized to extract a few key events. In the third stage, the Bayesian network is utilized to obtain the high level semantic events. Developing the Bayesian network for which the structure is assessed utilizing the Chowliu tree is the essential part of the technique. By applying group of Copula, the joint distributions of random variables of the system are displayed. Some of events that can be perceived by this technique in Soccer videos are objective, card, corners, shots on goals, offside, fouls, and missed shots. The users are more inclined to be interested in these events and not in complete and vast videos.

Keywords: Bayesian network, video summarization, semantic video analysis

Edition: Volume 3 Issue 12, December 2014,

Pages: 1692 - 1695

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