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Optimization Research on Deep Learning and Temporal Segmentation Algorithm of Video Shot in Basketball Games
Computational Intelligence and Neuroscience ( IF 3.120 ) Pub Date : 2021-09-07 , DOI: 10.1155/2021/4674140
Zhenggang Yan 1 , Yue Yu 2 , Mohammad Shabaz 3, 4
Affiliation  

The analysis of the video shot in basketball games and the edge detection of the video shot are the most active and rapid development topics in the field of multimedia research in the world. Video shots’ temporal segmentation is based on video image frame extraction. It is the precondition for video application. Studying the temporal segmentation of basketball game video shots has great practical significance and application prospects. In view of the fact that the current algorithm has long segmentation time for the video shot of basketball games, the deep learning model and temporal segmentation algorithm based on the histogram for the video shot of the basketball game are proposed. The video data is converted from the RGB space to the HSV space by the boundary detection of the video shot of the basketball game using deep learning and processing of the image frames, in which the histogram statistics are used to reduce the dimension of the video image, and the three-color components in the video are combined into a one-dimensional feature vector to obtain the quantization level of the video. The one-dimensional vector is used as the variable to perform histogram statistics and analysis on the video shot and to calculate the continuous frame difference, the accumulated frame difference, the window frame difference, the adaptive window’s mean, and the superaverage ratio of the basketball game video. The calculation results are combined with the set dynamic threshold to optimize the temporal segmentation of the video shot in the basketball game. It can be seen from the comparison results that the effectiveness of the proposed algorithm is verified by the test of the missed detection rate of the video shots. According to the test result of the split time, the optimization algorithm for temporal segmentation of the video shot in the basketball game is efficiently implemented.

中文翻译:

篮球比赛视频镜头深度学习与时间分割算法优化研究

篮球比赛视频镜头的分析和视频镜头的边缘检测是当今国际多媒体研究领域最活跃、发展最快的课题。视频镜头的时间分割基于视频图像帧提取。这是视频应用的前提。研究篮球比赛视频镜头的时间分割具有重要的现实意义和应用前景。针对目前算法对篮球比赛视频镜头分割时间较长的问题,提出了基于直方图的篮球比赛视频镜头深度学习模型和时间分割算法。通过深度学习对篮球比赛视频镜头进行边界检测并处理图像帧,将视频数据从RGB空间转换到HSV空间,其中使用直方图统计来降低视频图像的维度,将视频中的三色分量组合成一维特征向量,得到视频的量化级别。以一维向量为变量,对视频镜头进行直方图统计分析,计算连续帧差值、累积帧差值、窗口帧差值、自适应窗口均值、篮球超均值比游戏视频。计算结果与设定的动态阈值相结合,对篮球比赛视频镜头的时间分割进行优化。从对比结果可以看出,通过视频镜头漏检率的测试,验证了所提算法的有效性。根据分割时间的测试结果,有效地实现了篮球比赛视频镜头时间分割的优化算法。
更新日期:2021-09-07
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