Distantly Supervised Semantic Text Detection and Recognition for Broadcast Sports Videos Understanding

被引：0

作者：

Shah, Avijit ^{[1
]}

Biswas, Topojoy ^{[1
]}

Ramadoss, Sathish ^{[1
]}

Shah, Deven Santosh ^{[1
,2
]}

机构：

[1] Yahoo Res, Sunnyvale, CA 94089 USA

[2] Microsoft, Mountain View, CA USA

来源：

PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA, MM 2021 | 2021年

关键词：

datasets; neural networks; text detection; text recognition; event; detection;

D O I：

10.1145/3474085.3481543

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Comprehensive understanding of key players and actions in multiplayer sports broadcast videos is a challenging problem. Unlike in news or finance videos, sports videos have limited text. While both action recognition for multiplayer sports and detection of players has seen robust research, understanding contextual text in video frames still remains one of the most impactful avenues of sports video understanding. In this work we study extremely accurate semantic text detection and recognition in sports clocks, and challenges therein. We observe unique properties of sports clocks, which makes it hard to utilize general-purpose pre-trained detectors and recognizers, so that text can be accurately understood to the degree of being used to align to external knowledge. We propose a novel distant supervision technique to automatically build sports clock datasets. Along with suitable data augmentations, combined with any state-of-the-art text detection and recognition model architectures, we extract extremely accurate semantic text. Finally, we share our computational architecture pipeline to scale this system in industrial setting and proposed a robust dataset for the same to validate our results.

引用

页码：1167 / 1175

页数：9