Indexing of NFL video using MPEG-7 descriptors and MFCC features

被引：0

作者：

Quadri, SG ^{[1
]}

Krishnan, S ^{[1
]}

Guan, L ^{[1
]}

机构：

[1] Ryerson Univ, Dept Elect & Comp Engn, Toronto, ON M5B 2K3, Canada

来源：

2005 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1-5: SPEECH PROCESSING | 2005年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we propose an application system to classify American Football (NFL) Video shots into 4 categories, namely: Pass plays, Run plays, Field Goal/Extra Point plays (FG/XP) and Kickoff/Punt plays (K/P). The proposed system consists of two stages. The first stage is responsible for play event localization and the latter stage is responsible for feature mapping and classification. For play event localization we have proposed an algorithm that uses MPEG-7 motion activity descriptor and mean of the magnitudes of motion vectors, in a collaborative manner to detect the starting point of a play event within a video shot with 83% accuracy. The indexing and classification stage uses MPEG-7 motion and audio descriptors along with Mel Frequency Cepstrum Coefficients (MFCC) features to classify the events into 4 categories using Fisher's LDA. We obtain indexing accuracy of 92.5% by using leave-one-out classification technique on a database of 200 video shots taken from 4 different games obtained from 4 different networks.

引用

页码：429 / 432

页数：4