Advertisement

Training Sample Generation Software

  • Anton Vostrikov
  • Stanislav ChernyshevEmail author
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 143)

Abstract

This paper proposes new software for images annotations in order to make training samples. It can be applicable for artificial intelligent systems in areas such as human behavior analysis, identifying nonstandard patterns in human behavior, or situations leading to accidents, while driving a vehicle, operating a train, piloting an aircraft, etc. Also, the main requirements for newly developed applications for image annotation are formulated.

Keywords

Deep learning Training set Software for image annotation 

References

  1. 1.
    Statistics of traffic accidents (in Russian). http://www.1gai.ru/521669-statistika-dtp-v-rossii-za-yanvar-noyabr-2018-goda.html. Last accessed 25 Dec 2018
  2. 2.
    Kaggle: Your Home for Data Science. https://www.kaggle.com/. Last accessed 21 Dec 2018
  3. 3.
    Open Images Dataset V4. https://storage.googleapis.com/openimages/web/index.html. Last accessed 01 Dec 2018
  4. 4.
    Image Database ImageNet. http://www.image-net.org. Last accessed 02 Dec 2018
  5. 5.
    Caffe. Deep Learning Framework. http://caffe.berkeleyvision.org/. Last accessed 22 Dec 2018
  6. 6.
    TensorFlow. An Open Source Machine Learning Framework for Everyone. https://www.tensorflow.org/. Last accessed 22 Dec 2018
  7. 7.
    Kaftannikov, I.L., Parasich, A.V.: Problems of training set’s formation in machine learning tasks. In: Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control, Radio Electronics, vol. 16, no. 3, pp. 15–24 (2016)CrossRefGoogle Scholar
  8. 8.
    Amazon Mechanical Turk. https://www.mturk.com/mturk/welcome. Last accessed 27 Dec 2018
  9. 9.
    LabelIm. Open Annotation Tool. http://labelme.csail.mit.edu/Release3.0. Last accessed 30 Nov 2018
  10. 10.
    RectLabel. An Image Annotation Tool to Label Images for Bounding Box Object Detection and Segmentation. https://rectlabel.com. Last accessed 15 Dec 2018
  11. 11.
    LabelMe. http://labelme.csail.mit.edu/Release3.0. Last accessed 19 Dec 2018
  12. 12.
    Sloth. https://github.com/cvhciKIT/sloth. Last accessed 29 Dec 2018
  13. 13.
    Computer Vision Annotation Tool (CVAT). https://github.com/opencv/cvat. http://www.cs.columbia.edu/~vondrick/vatic. Last accessed 27 Nov 2018
  14. 14.
    Vondrick, C., Patterson, D., Ramanan, D.: Efficiently scaling up crowdsourced video annotation. Int. J. Comput. Vis. (IJCV) (2012)Google Scholar
  15. 15.
    Anno-M: A Semi Automatic Image Annotation Tool. https://github.com/virajmavani/semi-auto-image-annotation-tool. Last accessed 27 Dec 2018
  16. 16.
    Labelbox: The Best Way to Create and Manage Training Data. https://www.labelbox.com/. Last accessed 27 Dec 2018
  17. 17.
    Open Source Computer Vision Library. https://opencv.org. Last accessed 2018/11/16
  18. 18.
    Qt. https://www1.qt.io/ru/. Last accessed 12 Jan 2018
  19. 19.
    Chernyshev, S.A.: Modeling environment for processing of photos and video content transmitting by open channels. In: 2018 Wave Electronics and its Application in Information and Telecommunication Systems (WECONF), pp. 272–276. SUAI, Saint-Petersburg, Russia (2018)Google Scholar
  20. 20.
    Kurtyanik, D.V., Chernyshev, S.A.: Development of plugins for the modeling environment for stages of processing of photo and video images transmitted by open channels. In: 2018 Wave Electronics and its Application in Information and Telecommunication Systems (WECONF), pp. 319–324. SUAI, Saint-Petersburg, Russia (2018)Google Scholar
  21. 21.
    Torch. A scientific computing framework for LuaJIT. http://torch.ch. Last accessed 15 Dec 2018

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Saint-Petersburg State University of Aerospace InstrumentationSaint-PetersburgRussian Federation

Personalised recommendations