学习深度C3D功能的足球视频事件检测

Muhammad Zeeshan Khan, Summra Saleem, Muhammad A. Hassan, Muhammad Usman Ghanni Khan
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引用次数: 16

摘要

在过去的几十年里,足球视频事件识别一直是一个有趣的研究课题。许多机器学习技术和C2D(卷积二维)已经用于这个问题,但C3D还没有实现这个任务。利用C3D (Convolution 3-dimensional)技术,充分利用时空关系,构建深度卷积网络来突出不同的视频事件。首先,我们利用像素差和边缘变化率检测足球视频事件标记。在此基础上,提取了分割帧的语义特征,然后用CNN映射足球事件类别:角球、射门、射门尝试、点球。由于没有有效且合适的数据集,我们将足球视频分为四类,并开发了足球视频数据集用于训练CNN网络。对足球比赛片段的评价结果生成效率高。
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Learning Deep C3D Features For Soccer Video Event Detection
Soccer video event identification has been an interesting task in research community during past few decades. Numerous machine learning techniques and C2D (Convolution 2-dimensional) have been used for this problem, but C3D has not been implemented for this task. By taking advantage from C3D (Convolution 3-dimensional), to completely exploit spatio-temporal relation, deep convolution network is developed to highlight distinct video events in proposed research work. Initially, we detect soccer video event marks by pixel differencing and edge change ratio. After this semantic features of segmented frames are extracted followed by CNN to map soccer event categories: Corner, Shoot, Goal Attempt, Penalty Kick. Because no effective and suitable dataset is available, we categorized soccer videos into four classes and developed soccer videos dataset for training CNN network. Evaluation results on soccer match clips generated results with high efficiency.
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