TmoTA: Simple, Highly Responsive Tool for Multiple Object Tracking Annotation

Author:

Oyshi Marzan Tasnim1ORCID,Vogt Sebastian1ORCID,Gumhold Stefan2ORCID

Affiliation:

1. Computer Graphics and Visualization Lab (CGV); TU Dresden, Institute of Software and Multimedia Technology, Germany and TU Dresden, Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, Germany

2. Computer Graphics and Visualization Lab (CGV); TU Dresden, Institute of Software and Multimedia Technology, Germany and TU Dresden, Centre for Tactile Internet with Human-in-the-Loop (CeTI), Germany

Funder

German Federal Ministry of Education and Research (BMBF, 01/S18026A-F) by funding the Center for Scalable Data Analytics and Artificial Intelligance ScaDS.AI Dresden/Leipzig

Deutsche Forschungsgemeinschaft through DFG grant 389792660 as part of TRR 248 and the Clusters of Excellence CeTI (EXC 2050/1, grant 390696704)

Publisher

ACM

Reference24 articles.

1. K. Bernardin , A. Elbs , and R. Stiefelhagen . 2006. Multiple object tracking performance metrics and evaluation in a smart room environment . In Proceedings of the Sixth IEEE International Workshop on Visual Surveillance, VS 2006 , Graz, Austria, 1. May 2006 . K. Bernardin, A. Elbs, and R. Stiefelhagen. 2006. Multiple object tracking performance metrics and evaluation in a smart room environment. In Proceedings of the Sixth IEEE International Workshop on Visual Surveillance, VS 2006, Graz, Austria, 1. May 2006.

2. Keni Bernardin and Rainer Stiefelhagen . 2008. Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. J. Image Video Process . 2008 , Article 1 (jan 2008), 10 pages. https://doi.org/10.1155/2008/246309 10.1155/2008 Keni Bernardin and Rainer Stiefelhagen. 2008. Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. J. Image Video Process. 2008, Article 1 (jan 2008), 10 pages. https://doi.org/10.1155/2008/246309

3. Image annotation: Then and now

4. Dinesh Bolkensteyn. 2016. Vatic.js. https://github.com/dbolkensteyn/vatic.js. (Accessed on 12/08/2022). Dinesh Bolkensteyn. 2016. Vatic.js. https://github.com/dbolkensteyn/vatic.js. (Accessed on 12/08/2022).

5. cognilytica. 2021. Data Labeling Market: Research Snapshot Dec. 2021 - Cognilytica . https://www.cognilytica.com/document/data-labeling-market-research-snapshot-dec-2021/. (Accessed on 06/20/2022). cognilytica. 2021. Data Labeling Market: Research Snapshot Dec. 2021 - Cognilytica. https://www.cognilytica.com/document/data-labeling-market-research-snapshot-dec-2021/. (Accessed on 06/20/2022).

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3