...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Real-Time Image Processing >Adaptive pattern recognition in real-time video-based soccer analysis
【24h】

Adaptive pattern recognition in real-time video-based soccer analysis

机译：基于实时视频的足球分析中的自适应模式识别

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Computer-aided sports analysis is demanded by coaches and the media. Image processing and machine learning techniques that allow for "live" recognition and tracking of players exist. But these methods are far from collecting and analyzing event data fully autonomously. To generate accurate results, human interaction is required at different stages including system setup, calibration, supervision of classifier training, and resolution of tracking conflicts. Furthermore, the real-time constraints are challenging: in contrast to other object recognition and tracking applications, we cannot treat data collection, annotation, and learning as an offline task. A semi-automatic labeling of training data and robust learning given few examples from unbalanced classes are required. We present a real-time system acquiring and analyzing video sequences from soccer matches. It estimates each player's position throughout the whole match in real-time. Performance measures derived from these raw data allow for an objective evaluation of physical and tactical profiles of teams and individuals. The need for precise object recognition, the restricted working environment, and the technical limitations of a mobile setup are taken into account. Our contribution is twofold: (1) the deliberate use of machine learning and pattern recognition techniques allows us to achieve high classification accuracy in varying environments. We systematically evaluate combinations of image features and learning machines in the given online scenario. Switching between classifiers depending on the amount of training data and available training time improves robustness and efficiency. (2) A proper human-machine interface decreases the number of required operators who are incorporated into the system's learning process. Their main task reduces to the identification of players in uncertain situations. Our experiments showed high performance in the classification task achieving an average error rate of 3 % on three real-world datasets. The system was proved to collect accurate tracking statistics throughout different soccer matches in real-time by incorporating two human operators only. We finally show how the resulting data can be used instantly for consumer applications and discuss further development in the context of behavior analysis.
机译：教练和媒体需要计算机辅助的运动分析。存在允许“实时”识别和跟踪玩家的图像处理和机器学习技术。但是，这些方法远不能完全自主地收集和分析事件数据。为了生成准确的结果，需要在不同阶段进行人机交互，包括系统设置，校准，分类器培训的监督以及跟踪冲突的解决。此外，实时约束具有挑战性：与其他对象识别和跟踪应用程序相比，我们不能将数据收集，注释和学习视为脱机任务。给出训练数据的半自动标记和强大的学习能力，其中需要一些不平衡类的示例。我们提供了一个实时系统，可从足球比赛中获取和分析视频序列。它实时估计整个比赛中每个球员的位置。从这些原始数据得出的绩效指标可以客观评估团队和个人的身体和战术状况。考虑了对精确对象识别的需求，受限的工作环境以及移动设备的技术限制。我们的贡献是双重的：（1）故意使用机器学习和模式识别技术使我们能够在变化的环境中实现较高的分类精度。在给定的在线场景中，我们系统地评估图像特征和学习机的组合。根据训练数据的数量和可用训练时间在分类器之间进行切换可提高鲁棒性和效率。 （2）适当的人机界面减少了系统学习过程中所需的操作员数量。他们的主要任务是减少不确定情况下的玩家识别。我们的实验表明，在分类任务中具有很高的性能，在三个真实数据集上的平均错误率达到3％。事实证明，该系统仅包含两名人工操作员，即可实时收集各种足球比赛的准确跟踪统计信息。最后，我们展示了如何将所得数据立即用于消费者应用程序，并讨论了行为分析上下文中的进一步开发。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号