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. 1. Introduction Replays in soccer broadcasts cover most important contents of the video. Quick development of video compression techniques led to huge compressed video archives. Compressed domain video analysis on these archived videos can result in efficient video processing frameworks. However, noisy features are main challenge in compressed video analysis. Nowadays, most sports broadcasters use logo transitions before and after replay shots [1–3]; however, detection of replay shots by slow-motion detection can bring robustness and generality to prevailing systems. Slow-motion replays could be produced by standard or high-speed cameras. Several approaches are proposed for slow-motion detection based on each production style. A slow-motion replay from a standard camera can be generated by repeating some normal frames or inserting morphed frames between two consecutive frames [4, 5]. Repeated or inserted frames result in special patterns in frame difference feature and could be detected easily in spatial [4–6] or compressed domain [7–9]. Recently, majority of broadcasters are using high-speed cameras for slow-motion generation to achieve finer presentation of fast movements. When high-speed cameras are used with recording frame rate higher than desired slow-motion frame rate, some frames must be dropped. Dropped frames could be detected by plentiful fluctuations in frame difference feature [10]. In addition, slow-motion replays generated by dropped frames has higher mean of absolute frame difference than slow-motion replays generated by normal cameras [4]. With a high-speed camera, the slow-motion effect can also be generated by simply playing out the video at the normal speed. We call these slow-motion replays generated by high-speed cameras HISM replays. Detection of HISMs is very challenging because they do not result in any special pattern in visual features. Han et al. in [11] tried to detect HISMs by exploiting camera motion patterns
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