Paper Info
Title
Sports Video Captioning via Attentive Motion Representation and Group Relationship Modeling
Abstract
AbstractSports video captioning refers to the task of automatically generating a textual description for sports events (football, basketball, or volleyball games). Although a great deal of previous work has shown promising performance in producing a coarse and a general description of a video but lack of professional sports knowledge, it is still quite challenging to caption a sports video with multiple fine-grained player’s actions and complex group relationship between players. In this paper, we present a novel hierarchical recurrent neural network-based framework with an attention mechanism for sports video captioning, in which a motion representation module is proposed to capture individual pose attribute and dynamical trajectory cluster information with extra professional sports knowledge, and a group relationship module is employed to design a scene graph for modeling players’ interaction by a gated graph convolutional network. Moreover, we introduce a new dataset called sports video captioning dataset-volleyball for evaluation. The proposed model is evaluated on three widely adopted public datasets and our collected new dataset, on which the effectiveness of our method is well demonstrated.
Year
DOI
Venue
2020
10.1109/TCSVT.2019.2921655
Periodicals
Keywords
DocType
Volume
Sports, Visualization, Trajectory, Semantics, Task analysis, Logic gates, Games, Sports video, video captioning, motion representation, group relationship, RNN
Journal
30
Issue
ISSN
Citations 
8
1051-8215
6
PageRank 
References 
Authors
0.70
29
4
Name
Order
Citations
PageRank
Mengshi Qi1363.91
Yunhong Wang23816278.50
Annan Li322214.22
Jiebo Luo46314374.00