Novel technique for broadcast footage overlay text recognition

Xie, Da; Depoian, Arthur C.; Jaques, Lorenzo E.; Bailey, Colleen P.; Guturu, Parthasarathy

doi:10.1117/12.2588177

Cited by 1 publication

(2 citation statements)

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“…Broadcast footage text recognition: In [25] authors describe a method of localizing broadcast text region using traditional vision techniques, while [11] authors showcase an architecture specific to the task of understanding clock text. Event detection for contextual information: Recent works on detecting actions in basketball (NBA) [20], baseball (MLB) [19] and soccer [2] attempt to solve the problem at a coarse-grained level.…”

Section: Related Workmentioning

confidence: 99%

“…For such alignment or linking to external knowledge bases, its critical that the limited pieces of semantic texts are properly understood in the clock. Instead of relying on fine grained image classification (to different teams, or times, as often done in case of jersey number identification of players) or any domain specific neural architecture, or any classical vision/geometric heuristic (for text localization as in [25]), we resort to accurate text region detection and text recognition methods (using well used model architectures for maintainability and ease of use in production environments), without getting large sets of humanly labelled sports clock domain training data. Domain-specific knowledge of correctness of detection and recognition can be utilized to mitigate requirement of hand labelled data.…”

Section: Approachmentioning

confidence: 99%

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

Shah

Biswas

Ramadoss

et al. 2021

Proceedings of the 29th ACM International Conference on Multimedia

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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. CCS CONCEPTS• Computing methodologies → Activity recognition and understanding; Visual content-based indexing and retrieval; Scene understanding; Object detection; Object recognition.

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