College of Physical Education, Qiqihar University, Qiqihar 161000, China.
Comput Intell Neurosci. 2022 Jul 12;2022:3540642. doi: 10.1155/2022/3540642. eCollection 2022.
The main task of football video analysis is to detect and track players. In this work, we propose a deep convolutional neural network-based football video analysis algorithm. This algorithm aims to detect the football player in real time. First, five convolution blocks were used to extract a feature map of football players with different spatial resolution. Then, features from different levels are combined together with weighted parameters to improve detection accuracy and adapt the model to input images with various resolutions and qualities. Moreover, this algorithm can be extended to a framework for detecting players in any other sports. The experimental results assure the effectiveness of our algorithm.
足球视频分析的主要任务是检测和跟踪球员。在这项工作中，我们提出了一种基于深度卷积神经网络的足球视频分析算法。该算法旨在实时检测足球运动员。首先，使用五个卷积块来提取具有不同空间分辨率的足球运动员特征图。然后，通过加权参数将来自不同层次的特征组合在一起，以提高检测精度并使模型适应具有各种分辨率和质量的输入图像。此外，该算法可以扩展到用于检测任何其他运动中球员的框架。实验结果证明了我们算法的有效性。
