Sports video captioning is a task of automatically generating a textual description for sports events (e.g. football, basketball or volleyball games). Although previous works have shown promising performance in producing the coarse and general description of a video, it is still quite challenging to caption a sports video with multiple fine-grained player's actions and complex group relationship among players. In this paper, we present a novel hierarchical recurrent neural network (RNN) based framework with an attention mechanism for sports video captioning. A motion representation module is proposed to extract individual pose attribute and group-level trajectory cluster information. Moreover, we introduce a new dataset called Sports Video Captioning Dataset-Volleyball for evaluation. We evaluate our proposed model over two public datasets and our new dataset, and the experimental results demonstrate that our method outperforms the state-of-the-art methods.
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