DocumentCode :
2674699
Title :
A novel framework of shot boundary detection for uncompressed videos
Author :
Hameed, Abdul
Author_Institution :
Dept. of Comput. Sci., COMSATS Inst. of Inf. Technol., Islamabad, Pakistan
fYear :
2009
fDate :
19-20 Oct. 2009
Firstpage :
274
Lastpage :
279
Abstract :
The automatic video shot detection is receiving a great impact with the advances in the digital video technology and ever increasing accessibility of computing results. In this paper we describe a framework for extracting shot detection by using the threshold values of diverse statistical features for raw video frames. Two different types of sports videos viz. soccer and basketball are used for assessment. The approach exploits correlation, maximum histogram difference and running average difference as the classifiers. The results are evaluated by selection of appropriate threshold of these features after training of framework. The winner take-all selection scheme is applied if correlation coefficient and histogram difference features are unable to identify the shot detection. Experimental results on divergent set of test videos reveal the effectiveness of this shot detection approach.
Keywords :
correlation methods; feature extraction; probability; video signal processing; automatic video shot detection; correlation analysis; digital video technology; maximum histogram difference; running average difference; shot detection extraction; sports videos; winner take-all selection scheme; Computer science; Data mining; Detection algorithms; Feature extraction; Gunshot detection systems; Histograms; Information technology; Machine learning algorithms; Testing; Videos; Shot Detection; Threshold Selection; Winner take-all selection; YUV Histogram Difference;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Emerging Technologies, 2009. ICET 2009. International Conference on
Conference_Location :
Islamabad
Print_ISBN :
978-1-4244-5630-7
Electronic_ISBN :
978-1-4244-5631-4
Type :
conf
DOI :
10.1109/ICET.2009.5353162
Filename :
5353162
Link To Document :
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