End -to -end video text detection with online tracking

doi:10.1016/j.patcog.2020.107791

CASIA OpenIR > 模式识别实验室

	End -to -end video text detection with online tracking
	Yu, Hongyuan1,2 ; Huang, Yan1,2 ; Pi, Lihong 3; Zhang, Chengquan 4; Li, Xuan 4; Wang, Liang1,2,5
发表期刊	PATTERN RECOGNITION
ISSN	0031-3203
	2021-05-01
卷号	113 页码:12
通讯作者	Wang, Liang(wangliang@nlpr.ia.ac.cn)
摘要	Text in videos usually acts as important semantic cues, which is helpful to video analysis. Video text detection is considered as one of the most difficult tasks in document analysis due to the following two challenges: 1) the difficulties caused by video scenes, i.e., motion blur, illumination changes, and occlusion; 2) the properties of text including variants of fonts, languages, orientations, and shapes. Most existing methods try to improve the video text detection through video text tracking, but treat these two tasks separately. This can significantly increase the amount of calculations and cannot take full advantage of the supervisory information of both tasks. In this work, we introduce explainable descriptor, combines appearance, geometry and PHOC features, to establish a bridge between detection and tracking and build an end-to-end video text detection model with online tracking to address these challenges together. By integrating these two branches into one trainable framework, they can promote each other and the computational cost is significantly reduced. Besides, the introduce explainable descriptor also make our end-to-end model have inherent interpretability. Experiments on existing video text benchmarks including ICDAR 2013 Video, DOST, Minetto and YVT verify the role of explainable descriptors in improving model expression ability and the proposed method significantly outperforms state-of-the-art methods. Our method improves F-score by more than 2% on all datasets and achieves 81 . 52% on the MOTA of the Minetto dataset. (c) 2021 Elsevier Ltd. All rights reserved.
关键词	End-to-end Video text detection Online tracking
DOI	10.1016/j.patcog.2020.107791
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2016YFB1001000] ; National Natural Science Foundation of China[61525306] ; National Natural Science Foundation of China[61633021] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[61420106015] ; National Natural Science Foundation of China[61806194] ; Capital Science and Technology Leading Talent Training Project[Z181100006318030] ; Beijing Science and Technology Project[Z181100008918010]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Capital Science and Technology Leading Talent Training Project ; Beijing Science and Technology Project
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000626268400011
出版者	ELSEVIER SCI LTD
七大方向——子方向分类	目标检测、跟踪与识别
引用统计	被引频次：13[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/44143
专题	模式识别实验室
通讯作者	Wang, Liang
作者单位	1.Univ Chinese Acad Sci UCAS, Beijing 100049, Peoples R China 2.Chinese Acad Sci CASIA, Inst Automat, Natl Lab Pattern Recognit NLPR, Ctr Res Intelligent Percept & Comp CRIPAC, Beijing 100190, Peoples R China 3.Tsinghua Univ THU, Inst Microelect, Beijing 100084, Peoples R China 4.Baidu Inc, Dept Comp Vis Technol VIS, Beijing 100085, Peoples R China 5.Chinese Acad Sci CASIA, Inst Automat, Ctr Excellence Brain Sci & Intelligence Technol C, Beijing 100190, Peoples R China
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室; 中国科学院自动化研究所
推荐引用方式 GB/T 7714	Yu, Hongyuan,Huang, Yan,Pi, Lihong,et al. End -to -end video text detection with online tracking[J]. PATTERN RECOGNITION,2021,113:12.
APA	Yu, Hongyuan,Huang, Yan,Pi, Lihong,Zhang, Chengquan,Li, Xuan,&Wang, Liang.(2021).End -to -end video text detection with online tracking.PATTERN RECOGNITION,113,12.
MLA	Yu, Hongyuan,et al."End -to -end video text detection with online tracking".PATTERN RECOGNITION 113(2021):12.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
PR_hongyuan.pdf（4997KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	浏览下载