  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.
