This paper introduces Deep HM-SORT, a novel online multi-object tracking
algorithm specifically designed to enhance the tracking of athletes in sports
scenarios. Traditional multi-object tracking methods often struggle with sports
environments due to the similar appearances of players, irregular and
unpredictable movements, and significant camera motion. Deep HM-SORT addresses
these challenges by integrating deep features, harmonic mean, and Expansion
IOU. By leveraging the harmonic mean, our method effectively balances
appearance and motion cues, significantly reducing ID-swaps. Additionally, our
approach retains all tracklets indefinitely, improving the re-identification of
players who leave and re-enter the frame. Experimental results demonstrate that
Deep HM-SORT achieves state-of-the-art performance on two large-scale public
benchmarks, SportsMOT and SoccerNet Tracking Challenge 2023. Specifically, our
method achieves 80.1 HOTA on the SportsMOT dataset and 85.4 HOTA on the
SoccerNet-Tracking dataset, outperforming existing trackers in key metrics such
as HOTA, IDF1, AssA, and MOTA. This robust solution provides enhanced accuracy
and reliability for automated sports analytics, offering significant
improvements over previous methods without introducing additional computational
cost.

该论文介绍了一种名为 Deep HM-SORT 的新型在线多目标跟踪算法，专门用于提高运动场景中运动员的跟踪。通过集成深度特征、谐波平均和 Expansion IOU，Deep HM-SORT 解决了传统多目标跟踪方法在体育环境中面临的挑战，有效平衡外观和动作线索，显著降低 ID 交换，并改进了离开和重进画面的运动员的再识别问题。实验结果表明，Deep HM-SORT 在两个大规模公共基准测试中取得了最先进的性能，分别在 SportsMOT 数据集上达到 80.1 HOTA 和在 SoccerNet-Tracking 数据集上达到 85.4 HOTA，在 HOTA、IDF1、AssA 和 MOTA 等关键指标上胜过现有的跟踪器。该稳健的解决方案为自动化体育分析提供了提高准确性和可靠性的显著改进，而且没有引入额外的计算成本。

深度 HM-SORT：运用深度特征、调和平均和扩展 IOU 提升运动多目标追踪

Deep HM-SORT: Enhancing Multi-Object Tracking in Sports with Deep  Features, Harmonic Mean, and Expansion IOU

We consider the problem of generating musical soundtracks in sync with
rhythmic visual cues. Most existing works rely on pre-defined music
representations, leading to the incompetence of generative flexibility and
complexity. Other methods directly generating video-conditioned waveforms
suffer from limited scenarios, short lengths, and unstable generation quality.
To this end, we present Long-Term Rhythmic Video Soundtracker (LORIS), a novel
framework to synthesize long-term conditional waveforms. Specifically, our
framework consists of a latent conditional diffusion probabilistic model to
perform waveform synthesis. Furthermore, a series of context-aware conditioning
encoders are proposed to take temporal information into consideration for a
long-term generation. Notably, we extend our model's applicability from dances
to multiple sports scenarios such as floor exercise and figure skating. To
perform comprehensive evaluations, we establish a benchmark for rhythmic video
soundtracks including the pre-processed dataset, improved evaluation metrics,
and robust generative baselines. Extensive experiments show that our model
generates long-term soundtracks with state-of-the-art musical quality and
rhythmic correspondence. Codes are available at
https://github.com/OpenGVLab/LORIS.

该论文介绍了一个称为 LORIS 的新框架，用于在长期条件下生成可靠的波形，以生成与节奏视觉提示同步的音乐配乐，并扩展其模型的适用性，从舞蹈到多种体育场景，如地面练习和花样滑冰。