Abstract
Slow-motion replays are content full segments of broadcast soccer videos. In this paper, we propose an efficient method for detection of slow-motion shots produced by high-speed cameras in soccer broadcasts. A rich set of color, motion, and cinematic features are extracted from compressed video by partial decoding of the MPEG-1 bitstream. Then, slow-motion shots are modeled by SVM classifiers for each shot class. A set of six full-match soccer games is used for training and evaluation of the proposed method. Our algorithm presents satisfactory results along with high speed for slow-motion detection in soccer videos.
Highlights
Replays in soccer broadcasts cover most important contents of the video
We propose an efficient framework for slowmotion detection in compressed MPEG-1 soccer videos
In [26], we proposed a method for motion vector reliability measurement and global motion estimation in compressed MPEG-1 sequences
Summary
Replays in soccer broadcasts cover most important contents of the video. Quick development of video compression techniques led to huge compressed video archives. Repeated or inserted frames result in special patterns in frame difference feature and could be detected in spatial [4,5,6] or compressed domain [7,8,9]. Wang in [12] used color and motion features to model HISMs with SVM classifiers and obtained 75% precision and 61% recall rates on sports videos. Yang and others in [13] improved the work of Wang et al in [12] and used HMM models They achieved 83% precision and 81% recall rates on soccer videos. We propose an efficient framework for slowmotion detection in compressed MPEG-1 soccer videos.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
