Automatically extracting highlights for TV Baseball programs
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Automatic detection of 'Goal' segments in basketball videos
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Structure analysis of soccer video with domain knowledge and hidden Markov models
Pattern Recognition Letters - Video computing
Automatic soccer video analysis and summarization
IEEE Transactions on Image Processing
A unified approach to shot change detection and camera motion characterization
IEEE Transactions on Circuits and Systems for Video Technology
Animation movies trailer computation
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Video abstraction: A systematic review and classification
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Motion entropy feature and its applications to event-based segmentation of sports video
EURASIP Journal on Advances in Signal Processing
Automatic multi-level summarizations generation based on basic semantic unit for sports video
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Content-aware dynamic timeline for video browsing
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Robust GME in encoded MPEG video
Proceedings of the 9th International Conference on Advances in Mobile Computing and Multimedia
Multi-touch based video selection with an audio emotional curve
CHI '12 Extended Abstracts on Human Factors in Computing Systems
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We present a soccer video abstraction method based on the analysis of the audio and video streams. This method could be applied to other sports as rugby or american football. The main contribution of this paper is the design of an unsupervised summarization method, and more specifically, the introduction of an efficient detector of excited speech segments. An excited commentary is supposed to correspond to an interesting moment of the game. It is simultaneously characterized by an increase of the pitch (or fundamental frequency) within the voiced segments and an increase of the energy supported by the harmonics of the pitch. The pitch is estimated from the autocorrelation function and its local increases are detected from a multiresolution technique. We introduce a specific energy measure for the voiced segments. A statistical analysis of the energy measures is performed to detect the most excited parts of the speech. A deterministic combination of excited speech detection, dominant color identification and camera motion analysis is then performed in order to discriminate between excited speech sequences of the game and excited speech sequences in commercials or in studio shots included in the processed TV programs. The method presented here does not need any learning stage. It has been tested on seven soccer videos for a total duration of almost 20 hours.