Detecting players from sports broadcast videos is essential for intelligent event analysis. However, existing methods assume fixed player categories, incapably accommodating the real-world scenarios where categories continue to evolve. Directly fine-tuning these methods on newly emerging categories also exist the catastrophic forgetting due to the non-stationary distribution. Inspired by recent research on incremental object detection (IOD), we propose a Refined Response Distillation (R^2D) method to effectively mitigate catastrophic forgetting for IOD tasks of the players. Firstly, we design a progressive coarse-to-fine distillation region dividing scheme, separating high-value and low-value regions from classification and regression responses for precise and fine-grained regional knowledge distillation. Subsequently, a tailored refined distillation strategy is developed on regions with varying significance to address the performance limitations posed by pronounced feature homogeneity in the IOD tasks of the players. Furthermore, we present the NBA-IOD and Volleyball-IOD datasets as the benchmark and investigate the IOD tasks of the players systematically. Extensive experiments conducted on benchmarks demonstrate that our method achieves state-of-the-art results.The code and datasets are available at https://github.com/beiyan1911/Players-IOD.

翻译：从体育转播视频中检测球员对于智能事件分析至关重要。然而，现有方法假设球员类别固定，无法适应类别不断演变的实际场景。直接在新出现类别上微调这些方法，会因非平稳分布导致灾难性遗忘。受近期增量目标检测（IOD）研究的启发，我们提出一种精细化响应蒸馏（R²D）方法，以有效缓解球员IOD任务中的灾难性遗忘。首先，我们设计了一种渐进式从粗到细的蒸馏区域划分方案，从分类和回归响应中分离出高价值与低价值区域，以实现精准且细粒度的区域知识蒸馏。随后，针对球员IOD任务中显著的特征同质性所导致的性能局限，我们开发了一种针对不同重要性区域的定制化精细蒸馏策略。此外，我们提出了NBA-IOD和Volleyball-IOD数据集作为基准，并系统性地研究了球员IOD任务。在基准数据集上进行的大量实验表明，我们的方法取得了最先进的结果。代码和数据集可在https://github.com/beiyan1911/Players-IOD获取。