Chakradhar Guntuboina, Aditya Porwal, Preety Jain, Hansa Shingrakhia
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引用次数: 9
摘要
本文提出了一种计算成本低廉的方法,用于自动提取关键事件,并利用计分板检测对体育视频进行后续总结。使用由1300张图像组成的数据库来训练基于监督学习的目标检测算法YOLO (You Only Look Once)。然后,对于视频的每一帧,一旦使用YOLO检测到计分板,计分板就会从图像中裁剪出来。在此之后,图像处理技术应用于裁剪的记分牌,以减少噪音和误报。最后,将处理后的图像通过OCR(光学字符识别器)得到分数。基于规则的算法在OCR的输出上运行,以生成基于游戏的关键事件的时间戳。所提出的方法最适合那些想要分析游戏并希望获得重要事件发生的精确时间戳的人。在德甲联赛、英超联赛、ICC WC 2019、IPL 2019和职业卡巴迪联赛的视频中测试了该设计的性能。模拟过程中F1得分平均为0.979。该算法在五个不同类别的三个独立游戏(足球,板球,卡巴迪)上进行训练。该设计使用python 3.7实现。
Video Summarization for Multiple Sports Using Deep Learning
This paper proposes a computationally inexpensive method for automatic key-event extraction and subsequent summarization of sports videos using scoreboard detection. A database consisting of 1300 images was used to train a supervised-learning based object detection algorithm, YOLO (You Only Look Once). Then, for each frame of the video, once the scoreboard was detected using YOLO, the scoreboard was cropped out of the image. After this, image processing techniques were applied on the cropped scoreboard to reduce noise and false positives. Finally, the processed image was passed through an OCR (Optical Character Recognizer) to get the score. A rule-based algorithm was run on the output of the OCR to generate the timestamps of key-events based on the game. The proposed method is best suited for people who want to analyse the games and want precise timestamps of the occurrence of important events. The performance of the proposed design was tested on videos of Bundesliga, English Premier League, ICC WC 2019, IPL 2019, and Pro Kabaddi League. An average F1 Score of 0.979 was achieved during the simulations. The algorithm is trained on five different classes of three separate games (Soccer, Cricket, Kabaddi). The design is implemented using python 3.7.