论文标题
评估广播冰球视频中的播放器跟踪的深度跟踪模型
Evaluating deep tracking models for player tracking in broadcast ice hockey video
论文作者
论文摘要
跟踪和识别玩家是基于计算机视觉冰球分析的重要问题。球员跟踪是一个具有挑战性的问题，因为曲棍球运动员的运动是快节奏和非线性的。在曲棍球广播视频中，还有重要的玩家玩家和玩家板闭塞，摄像头平板和缩放。事先发表的研究表演播放器跟踪，借助手工制作的功能，以供播放器检测和重新识别。尽管存在用于曲棍球玩家跟踪的商业解决方案，但据我们所知，未使用网络架构，培训数据或性能指标。目前，尚无公开的曲棍球运动员跟踪作品，利用最新的深度学习进步，同时还报告了文献中使用的当前准确度指标。因此，在本文中，我们比较和对比几种最先进的跟踪算法，并分析其在冰球中的性能和失败模式。
Tracking and identifying players is an important problem in computer vision based ice hockey analytics. Player tracking is a challenging problem since the motion of players in hockey is fast-paced and non-linear. There is also significant player-player and player-board occlusion, camera panning and zooming in hockey broadcast video. Prior published research perform player tracking with the help of handcrafted features for player detection and re-identification. Although commercial solutions for hockey player tracking exist, to the best of our knowledge, no network architectures used, training data or performance metrics are publicly reported. There is currently no published work for hockey player tracking making use of the recent advancements in deep learning while also reporting the current accuracy metrics used in literature. Therefore, in this paper, we compare and contrast several state-of-the-art tracking algorithms and analyze their performance and failure modes in ice hockey.
