A Novel Approach for Fast and Accurate Commercial Detection in H.264/AVC Bit Streams Based on Logo Identification
Identifieur interne : 000A28 ( Main/Exploration ); précédent : 000A27; suivant : 000A29A Novel Approach for Fast and Accurate Commercial Detection in H.264/AVC Bit Streams Based on Logo Identification
Auteurs : Klaus Schöffmann [Autriche] ; Mathias Lux [Autriche] ; Laszlo Böszörmenyi [Autriche]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2009.
Abstract
Abstract: Commercial blocks provide no extra value for video indexing, retrieval, archiving, or summarization of TV broadcasts. Therefore, automatic detection of commercial blocks is an important topic in the domain of multimedia information systems. We present a commercial detection approach which is based on logo detection performed in the compressed domain. The novelty of our approach is that by taking advantage of advanced features of the H.264/AVC coding, it is both significantly faster and more exact than existing approaches working directly on compressed data. Our approach enables removal of commercials in a fraction of real-time while achieving an average recall of 97.33% with an average precision of 99.31%. Moreover, due to its run-time performance, our approach can also be employed on low performance devices, for instance DVB recorders.
Url:
DOI: 10.1007/978-3-540-92892-8_13
Affiliations:
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<front><div type="abstract" xml:lang="en">Abstract: Commercial blocks provide no extra value for video indexing, retrieval, archiving, or summarization of TV broadcasts. Therefore, automatic detection of commercial blocks is an important topic in the domain of multimedia information systems. We present a commercial detection approach which is based on logo detection performed in the compressed domain. The novelty of our approach is that by taking advantage of advanced features of the H.264/AVC coding, it is both significantly faster and more exact than existing approaches working directly on compressed data. Our approach enables removal of commercials in a fraction of real-time while achieving an average recall of 97.33% with an average precision of 99.31%. Moreover, due to its run-time performance, our approach can also be employed on low performance devices, for instance DVB recorders.</div>
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