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Video Editor for Annotating Human Actions and Object Trajectories

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Intelligent Information and Database Systems (ACIIDS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9622))

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Abstract

A system for managing, annotating and editing video sequences is a necessary tool in research on recognition of human actions and tracking people or objects. In addition annotation process is complex and expensive, so some people try to use crowdsourced marketplace based tools to make this process cost effective. Such a tool, video editor for annotating human actions and object trajectories -VATRAC, is presented. It enables flexible viewing video sequences under selected configuration of annotation layers, adding and editing of annotations for actions and trajectories of the entire objects or selected parts of the objects. Video sequences can be queried according to a variety of criteria and preferences for example searching for subsequences annotated with the action class.

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References

  1. Smith, J.R., Lugeon, B.: A visual annotation tool for multimedia content description. In: Proceedings of the SPIE Photonics East, Internet Multimedia Management Systems (2000)

    Google Scholar 

  2. 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, 157–173 (2008)

    Article  Google Scholar 

  3. Korč, F., Schneider, D.: Annotation Tool. Technical report TR-IGG-P-2007-01, University of Bonn, Department of Photogrammetry (2007)

    Google Scholar 

  4. Grundmann, M., Kwatra, V., Han, M., Essa, I.: Efficient hierarchical graph based video segmentation. In: IEEE CVPR (2010)

    Google Scholar 

  5. Kulbacki, M., Segen, J., Wereszczyński, K., Gudyś, A.: VMASS: massive dataset of multi-camera video for learning, classification and recognition of human actions. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds.) ACIIDS 2014, Part II. LNCS, vol. 8398, pp. 565–574. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  6. Zhang, S., Staudt, E., Faltemier, T., Roy-Chowdhury, A.: A camera network tracking (CamNeT) dataset and performance baseline. In: IEEE Winter Conference on Applications of Computer Vision, Waikoloa Beach, Hawaii, January 2015

    Google Scholar 

  7. Chen, C.-C., Ryoo, M.S., Aggarwal, J.K.: UT-Tower dataset: aerial view activity classification challenge (2010). http://cvrc.ece.utexas.edu/SDHA2010/Aerial_View_Activity.html

  8. Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. Trans. Pattern Anal. Mach. Intell. 29(12), 2247–2253 (2007)

    Article  Google Scholar 

  9. SchÃijldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: Proceedings of the ICPR, pp. 32–36 (2004)

    Google Scholar 

  10. Soomro, K., Zamir, A.R., Shah, M.: UCF101: a dataset of 101 human action classes from videos in the wild. In: CRCV-TR-12-01, November 2012

    Google Scholar 

  11. Reddy, K.K., Shah, M.: Recognizing 50 human action categories of web videos. Mach. Vis. Appl. J. (MVAP) 24, 971–981 (2012)

    Article  Google Scholar 

  12. Liu, J., Luo, J., Shah, M.: Recognizing realistic actions from videos “in the Wild”. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) (2009)

    Google Scholar 

  13. Rodriguez, M.D., Ahmed, J., Shah, M.: Action MACH: a spatio-temporal maximum average correlation height filter for action recognition. In: Computer Vision and Pattern Recognition (2008)

    Google Scholar 

  14. Jain, M., Jegou, H., Bouthemy, P.: Better exploiting motion for better action recognition. In: CVPR (2013)

    Google Scholar 

  15. Laptev, I., Marszałek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: IEEE Conference on Computer Vision & Pattern Recognition (2008)

    Google Scholar 

  16. Marszałek, M., Laptev, I., Schmid, C.: Actions in context. In: IEEE Conference on Computer Vision & Pattern Recognition (2009)

    Google Scholar 

  17. Kipp, M.: ANVIL - a generic annotation tool for multimodal dialogue. In: Proceedings of the 7th European Conference on Speech Communication and Technology (Eurospeech), pp. 1367–1370 (2001)

    Google Scholar 

  18. Vondrick, C., Patterson, D., Ramanan, D.: Efficiently scaling up crowdsourced video annotation. Int. J. Comput. Vis. (IJCV) 101, 184–204 (2012)

    Article  Google Scholar 

  19. Hailpern, J.: VCode and VData: Illustrating a new Framework for Supporting the Video Annotation Workflow. Google engEDU: Tech Talks, Mountain View, CA, 21 June 2008

    Google Scholar 

  20. Swets, J.A.: Signal Detection Theory and ROC Analysis in Psychology and Diagnostics : Collected Papers. Lawrence Erlbaum Associates, Mahwah, NJ (1996)

    MATH  Google Scholar 

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Acknowledgments

This work has been supported by the National Centre for Research and Development (project UOD-DEM-1-183/001 “Intelligent video analysis system for behavior and event recognition in surveillance networks”).

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Correspondence to Marek Kulbacki .

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Kulbacki, M., Wereszczyński, K., Segen, J., Sachajko, M., Bąk, A. (2016). Video Editor for Annotating Human Actions and Object Trajectories. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9622. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49390-8_44

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  • DOI: https://doi.org/10.1007/978-3-662-49390-8_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49389-2

  • Online ISBN: 978-3-662-49390-8

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