An Efficient Framework for Automatic Highlights Generation from Sports Videos

This letter presents a framework for replay detection in sports videos to generate highlights. For replay detection, the proposed work exploits the following facts: 1) broadcasters introduce gradual transition (GT) effect both at the start and at the end of a replay segment (RS), and 2) the absence of score captions (SCs) in an RS. The dual-threshold-based method is used to detect GT frames from the input video. A pair of successive GT frames is used to extract the candidate RSs. All frames in the selected segment are processed to detect SC. To this end, temporal running average is used to filter out temporal variations. First- and second-order statistics are used to binarize the running average image, which is fed to optical character recognition stage for character recognition. The absence/presence of SC is used for replay/live frame labeling. The SC detection stage complements the GT detection process, therefore, a combination of both is expected to result in superior computational complexity and detection accuracy. The performance of the proposed system is evaluated on 22 videos of four different sports (e.g., Cricket, tennis, baseball, and basketball). Experimental results indicate that the proposed method can achieve average detection accuracy ≥ 94.7%.