论文标题
体育员跟踪中以中心和中央距离恢复为中心距离
Observation Centric and Central Distance Recovery on Sports Player Tracking
论文作者
论文摘要
随着对象检测和重新识别的发展，对人类的多对象跟踪已迅速改善。但是，即使对于最先进的跟踪算法，对具有相似外观和非线性运动的人类进行的多演员跟踪仍然非常具有挑战性。当前基于运动的跟踪算法通常使用Kalman滤波器来预测对象的运动，但是，当目标不线性移动时，其线性运动假设可能会导致跟踪失败。而且，对于在运动场上跟踪的多玩家，因为同一团队中的球员通常穿着相同的球衣，这使得在短期和长期跟踪过程中更加牢固地识别。在这项工作中，我们提出了一种基于运动的跟踪算法和三个针对三项运动在内的后处理管道，包括篮球，足球和排球，我们成功地处理了运动场上球员非线性运动的跟踪。实验导致ECCV DeeperAction挑战赛的测试集SportsMot数据集证明了我们的方法的有效性，该方法的HOTA达到了73.968，在2022年2022年SportsMot Workshop最终排行榜上排名第三。
Multi-Object Tracking over humans has improved rapidly with the development of object detection and re-identification. However, multi-actor tracking over humans with similar appearance and nonlinear movement can still be very challenging even for the state-of-the-art tracking algorithm. Current motion-based tracking algorithms often use Kalman Filter to predict the motion of an object, however, its linear movement assumption can cause failure in tracking when the target is not moving linearly. And for multi-players tracking over the sports field, because the players in the same team are usually wearing the same color of jersey, making re-identification even harder both in the short term and long term in the tracking process. In this work, we proposed a motionbased tracking algorithm and three post-processing pipelines for three sports including basketball, football, and volleyball, we successfully handle the tracking of the non-linear movement of players on the sports fields. Experiments result on the testing set of ECCV DeeperAction Challenge SportsMOT Dataset demonstrate the effectiveness of our method, which achieves a HOTA of 73.968, ranking 3rd place on the 2022 Sportsmot workshop final leaderboard.
