Open Source Multipurpose Multimedia Annotation Tool

  • Joed Lopes da SilvaEmail author
  • Alan Naoto Tabata
  • Lucas Cardoso Broto
  • Marta Pereira Cocron
  • Alessandro Zimmer
  • Thomas Brandmeier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12131)


Efficient tools and frameworks for image and video annotation become more necessary for pattern recognition and computer vision research as datasets for training and testing of algorithms get increasingly larger. Different software packages have been developed to deal with these tasks, but they are usually designed for specific demands, problems or are not open to the public. This paper presents an open source multipurpose tool for annotation on multimedia datasets with extended flexibility through customizable labels, option of working on a shared database for collaborative annotation and with special care given on usability and efficiency for the best user experience. The Annotation Tool is available in the following link:


Open source Video annotation Multipurpose Generic annotation 


  1. 1.
    Ambardekar, A., Nicolescu, M., Dascalu, S.: Ground truth verification tool (GTVT) for video surveillance systems. In: 2009 Second International Conferences on Advances in Computer-Human Interactions, pp. 354–359. IEEE (2009)Google Scholar
  2. 2.
    Boersma, P., et al.: The use of praat in corpus research. In: The Oxford Handbook of Corpus Phonology, pp. 342–360 (2014)Google Scholar
  3. 3.
    Doermann, D., Mihalcik, D.: Tools and techniques for video performance evaluation. In: Proceedings 15th International Conference on Pattern Recognition, ICPR-2000, vol. 4, pp. 167–170. IEEE (2000)Google Scholar
  4. 4.
    Dutta, A., Zisserman, A.: The VIA annotation software for images, audio and video. In: Proceedings of the 27th ACM International Conference on Multimedia, MM 2019. ACM, New York (2019).
  5. 5.
    Jaynes, C., Webb, S., Steele, R., Xiong, Q.: An open development environment for evaluation of video surveillance systems. In: PETS02, pp. 32–39 (2002)Google Scholar
  6. 6.
    Kavasidis, I., Palazzo, S., Di Salvo, R., Giordano, D., Spampinato, C.: A semi-automatic tool for detection and tracking ground truth generation in videos. In: Proceedings of the 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications, p. 6. ACM (2012)Google Scholar
  7. 7.
    Kavasidis, I., Palazzo, S., Salvo, R.D., Giordano, D., Spampinato, C.: An innovative web-based collaborative platform for video annotation. Multimed. Tools Appl. 70(1), 413–432 (2013). Scholar
  8. 8.
    Kipp, M.: Anvil: the video annotation research tool. In: Handbook of Corpus Phonology, pp. 420–436 (2014)Google Scholar
  9. 9.
  10. 10.
    Lausberg, H., Sloetjes, H.: Coding gestural behavior with the NEUROGES-ELAN system. Behav. Res. Methods 41(3), 841–849 (2009). Scholar
  11. 11.
    Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). Scholar
  12. 12.
    Llaneras, R.E., Salinger, J., Green, C.A.: Human factors issues associated with limited ability autonomous driving systems: drivers’ allocation of visual attention to the forward roadway (2013)Google Scholar
  13. 13.
    MacMullen, W.J.: Annotation as process, thing, and knowledge: multi-domain studies of structured data annotation. In: ASIST Annual Meeting (2005)Google Scholar
  14. 14.
    Maurer, M., Gerdes, J.C., Lenz, B., Winner, H., et al.: Autonomous Driving. Springer, Heidelberg (2016). Scholar
  15. 15.
    Muhrer, E., Reinprecht, K., Vollrath, M.: Driving with a partially autonomous forward collision warning system: how do drivers react? Hum. Factors 54(5), 698–708 (2012)CrossRefGoogle Scholar
  16. 16.
    Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. Int. J. Comput. Vis. 77(1–3), 157–173 (2008). Scholar
  17. 17.
    da Silva, J.L., Thomas Brandmeier, A.Z.: Automatic measurement of automobile drivers attention level via computer vision. In: XXIV Congresso Brasileiro De Engenharia Biomédica (2014)Google Scholar
  18. 18.
    Spampinato, C., Boom, B., He, J.: First international workshop on visual interfaces for ground truth collection in computer vision applications. In: Proceedings of the International Working Conference on Advanced Visual Interfaces, pp. 812–814. ACM (2012)Google Scholar
  19. 19.
    Spampinato, C., Boom, B., Huet, B.: Vigta 2013: Proceedings of the International Workshop on Video and Image Ground Truth in Computer Vision Applications, pp. 812–814. ACM (2013)Google Scholar
  20. 20.
  21. 21.
  22. 22.
    Vondrick, C., Patterson, D., Ramanan, D.: Efficiently scaling up crowdsourced video annotation. Int. J. Comput. Vis. 101(1), 184–204 (2013). Scholar
  23. 23.
    VoTT: Vott (visual object tagging tool) (2019).
  24. 24.
    Walch, M., Lange, K., Baumann, M., Weber, M.: Autonomous driving: investigating the feasibility of car-driver handover assistance. In: Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 11–18. ACM (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Joed Lopes da Silva
    • 1
    Email author
  • Alan Naoto Tabata
    • 1
    • 2
  • Lucas Cardoso Broto
    • 1
    • 2
  • Marta Pereira Cocron
    • 1
  • Alessandro Zimmer
    • 1
    • 2
  • Thomas Brandmeier
    • 1
  1. 1.Research and Test Center CARISSMATechnische Hochschule IngolstadtIngolstadtGermany
  2. 2.Federal University of ParanaCuritibaBrazil

Personalised recommendations