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

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributedpeer-review


Machine learning is applied in a multitude of sectors with very impressive results. This success is due to the availability of an ever-growing amount of data acquired by omnipresent sensor devices and platforms on the internet. But there is a scarcity of labeled data which is required for most ML methods. However, generation of labeled data requires much time and resources. In this paper, we propose a portable, Open Source, simple and responsive manual Tool for 2D multiple object Tracking Annotation (TmoTA). Besides responsiveness, our tool design provides several features like view centering and looped playback that speed up the annotation process. We evaluate our proposed tool by comparing TmoTA with the widely used manual labeling tools CVAT, Label Studio, and two semi-automated tools Supervisely and VATIC with respect to object labeling time and accuracy. The evaluation includes a user study and pre-case studies showing that the annotation time per object frame can be reduced by 20% to 40% over the first 20 annotated objects compared to the manual labeling tools.


Original languageEnglish
Title of host publicationCHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
EditorsAlbrecht Schmidt, Kaisa Väänänen, EditoTesh Goyal, Per Ola Kristensson, Anicia Peters, Stefanie Mueller, Julie R. Williamson, Max L. Wilson
PublisherAssociation for Computing Machinery
Number of pages11
ISBN (electronic)978-1-4503-9421-5
Publication statusPublished - 19 Apr 2023

Publication series

SeriesCHI: Conference on Human Factors in Computing Systems


TitleCHI Conference on Human Factors in Computing Systems 2023
Abbreviated titleCHI 2023
Duration23 - 28 April 2023
LocationCongress Center Hamburg (CCH) & online



  • data labeling, manual labeling, video sequence labeling

Library keywords