Abstract
The TrackNet series has established a strong baseline for fast-moving small object tracking in sports. However, existing iterations face significant limitations: V1-V3 struggle with occlusions due to a reliance on purely visual cues, while TrackNetV4, despite introducing motion inputs, suffers from directional ambiguity as its absolute difference method discards motion polarity. To overcome these bottlenecks, we propose TrackNetV5, a robust architecture integrating two novel mechanisms. First, to recover lost directional priors, we introduce the Motion Direction Decoupling (MDD) module. Unlike V4, MDD decomposes temporal dynamics into signed polarity fields, explicitly encoding both movement occurrence and trajectory direction. Second, we propose the Residual-Driven Spatio-Temporal Refinement (R-STR) head. Operating on a coarse-to-fine paradigm, this Transformer-based module leverages factorized spatio-temporal contexts to estimate a corrective residual, effectively recovering occluded targets. Extensive experiments on the TrackNetV2 dataset demonstrate that TrackNetV5 achieves a new state-of-the-art F1-score of 0.9859 and an accuracy of 0.9733, significantly outperforming previous versions. Notably, this performance leap is achieved with a marginal 3.7% increase in FLOPs compared to V4, maintaining real-time inference capabilities while delivering superior tracking precision.
Abstract (translated)
TrackNet系列为体育赛事中快速移动的小物体跟踪建立了强大的基准。然而，现有的版本面临一些显著的限制：V1-V3由于完全依赖于视觉线索，在处理遮挡时表现不佳；而TrackNetV4虽然引入了运动输入，但由于其绝对差分方法放弃了运动极性信息，导致方向模糊问题依然存在。为克服这些瓶颈，我们提出了TrackNetV5这一稳健架构，集成了两种新颖的机制。 首先，为了恢复丢失的方向先验知识，我们介绍了运动方向解耦（MDD）模块。不同于V4版本，MDD将时间动力学分解成带有符号极性场的形式，明确编码了移动的发生和轨迹方向。其次，我们提出了残差驱动的空间-时间细化（R-STR）头部。这一基于Transformer的模块采用从粗到细的工作模式，利用因子化的时间-空间上下文来估计校正残差，从而有效恢复被遮挡的目标。 在TrackNetV2数据集上的广泛实验表明，TrackNetV5达到了0.9859的新F1评分和0.9733的精度，显著优于之前的版本。值得注意的是，这种性能飞跃仅伴随着与V4相比微小的3.7%浮点运算（FLOPs）增加，在保持实时推理能力的同时提供了更精确的跟踪效果。
URL
https://arxiv.org/abs/2512.02789
