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Learning to Track and Identify Players from Broadcast Sports Videos

机译:学习从广播体育视频中跟踪和识别运动员

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Tracking and identifying players in sports videos filmed with a single pan-tilt-zoom camera has many applications, but it is also a challenging problem. This paper introduces a system that tackles this difficult task. The system possesses the ability to detect and track multiple players, estimates the homography between video frames and the court, and identifies the players. The identification system combines three weak visual cues, and exploits both temporal and mutual exclusion constraints in a Conditional Random Field (CRF). In addition, we propose a novel Linear Programming (LP) Relaxation algorithm for predicting the best player identification in a video clip. In order to reduce the number of labeled training data required to learn the identification system, we make use of weakly supervised learning with the assistance of play-by-play texts. Experiments show promising results in tracking, homography estimation, and identification. Moreover, weakly supervised learning with play-by-play texts greatly reduces the number of labeled training examples required. The identification system can achieve similar accuracies by using merely 200 labels in weakly supervised learning, while a strongly supervised approach needs a least 20,000 labels.
机译:跟踪和识别用单个平移-变焦变焦摄像机拍摄的体育视频中的运动员具有许多应用,但这也是一个难题。本文介绍了一种解决这一难题的系统。该系统具有检测和跟踪多个玩家,估计视频帧与球场之间的单应性以及识别玩家的能力。识别系统结合了三个弱视觉提示,并在条件随机场(CRF)中利用时间和互斥约束。此外,我们提出了一种新颖的线性规划(LP)松弛算法,用于预测视频剪辑中的最佳播放器标识。为了减少学习识别系统所需的带有标签的训练数据的数量,我们在逐次播放文本的帮助下利用了弱监督学习。实验表明,在跟踪,单应性估计和识别方面有希望的结果。此外,通过逐项播放的文本进行弱监督学习大大减少了所需的带标签的训练示例的数量。识别系统可以通过在弱监督学习中仅使用200个标签来实现类似的准确性,而强监督方法则需要至少20,000个标签。

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