skip to main content
10.1145/3204949.3208117acmconferencesArticle/Chapter ViewAbstractPublication PagesmmsysConference Proceedingsconference-collections
demonstration

Visual object tracking in a parking garage using compressed domain analysis

Authors Info & Claims
Published:12 June 2018Publication History

ABSTRACT

Modern driver assistance systems enable a variety of use cases which rely on accurate localization information of all traffic participants. Due to the unavailability of satellite-based localization, the use of infrastructure cameras is a promising alternative in indoor spaces such as parking garages. This paper presents a parking management system which extends the previous work of the eValet system with a low-complexity tracking functionality on compressed video bitstreams (compressed-domain tracking). The advantages of this approach include the improved robustness to partial occlusions as well as a resource-efficient processing of compressed video bit-streams. We have separated the tasks into different modules which are integrated into a comprehensive architecture. The demonstrator setup includes a 2D visualizer illustrating the operation of the algorithms on a single camera stream and a 3D visualizer displaying the abstract object detections in a global reference frame.

References

  1. M. Ahad, J. Tan, H. Kim, and S. Ishikawa. 2012. Motion history image: its variants and applications. Machine Vision and Applications 23, 2 (2012), 255--281. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. R.-V. Babu, M. Tom, and P. Wadekar. 2016. A survey on compressed domain video analysis techniques. Multimedia Tools and Applications 75, 2 (2016), 1043--1078. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. D. Becker, J. Einsiedler, B. Schäufele, A. Binder, and I. Radusch. 2013. Identification of vehicle tracks and association to wireless endpoints by multiple sensor modalities. In International Conference on Indoor Positioning and Indoor Navigation. 1--10.Google ScholarGoogle Scholar
  4. D. Becker, A. Munjere, J. Einsiedler, K. Massow, F. Thiele, and I. Radusch. 2016. Blurring the border between real and virtual parking environments. In 2016 IEEE Intelligent Vehicles Symposium (IV). 1205--1210.Google ScholarGoogle Scholar
  5. D. Becker, B. Schäufele, J. Einsiedler, O. Sawade, and I. Radusch. 2014. Vehicle and pedestrian collision prevention system based on smart video surveillance and C2I communication. In Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on. IEEE, 3088--3093.Google ScholarGoogle Scholar
  6. D. Becker, F. Thiele, O. Sawade, and I. Radusch. 2015. Cost-effective camera based ground truth for indoor localization. In 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). 885--890.Google ScholarGoogle Scholar
  7. J. Einsiedler, D. Becker, and I. Radusch. 2014. External visual positioning system for enclosed carparks. In Positioning, Navigation and Communication (WPNC), 2014 11th Workshop on. IEEE, 1--6.Google ScholarGoogle Scholar
  8. J. Einsiedler, I. Radusch, and K. Wolter. 2017. Vehicle indoor positioning: A survey. In 2017 14th Workshop on Positioning, Navigation and Communications (WPNC). 1--6.Google ScholarGoogle Scholar
  9. A. Goldberg, S. Hed, H. Kaplan, P. Kohli, R. Tarjan, and R. Werneck. 2015. Faster and more dynamic maximum flow by incremental breadth-first search. In Algorithms-ESA 2015. Springer, 619--630.Google ScholarGoogle Scholar
  10. S. Gül, J. T. Meyer, C. Hellge, T. Schierl, and W. Samek. 2016. Hybrid video object tracking in H.265/HEVC video streams. In Multimedia Signal Processing (MMSP), 2016 IEEE 18th International Workshop on. IEEE, 1--5.Google ScholarGoogle Scholar
  11. A. Ibisch, S. Houben, M. Michael, R. Kesten, and F. Schuller. 2015. Arbitrary object localization and tracking via multiple-camera surveillance system embedded in a parking garage. In Video Surveillance and Transportation Imaging Applications 2015, Vol. 9407. International Society for Optics and Photonics, 94070G.Google ScholarGoogle Scholar
  12. A. Ibisch, S. Stümper, H. Altinger, M. Neuhausen, M. Tschentscher, M. Schlipsing, J. Salinen, and A. Knoll. 2013. Towards autonomous driving in a parking garage: Vehicle localization and tracking using environment-embedded lidar sensors. In Intelligent Vehicles Symposium (IV), 2013 IEEE. IEEE, 829--834.Google ScholarGoogle Scholar
  13. S. Khatoonabadi and I. Bajić. 2013. Video object tracking in the compressed domain using spatio-temporal Markov random fields. IEEE Transactions on Image Processing 22, 1 (2013), 300--313. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. R. Lienhart and J. Maydt. 2002. An extended set of Haar-like features for rapid object detection. In Proceedings. International Conference on Image Processing, Vol. 1. I-900--I-903 vol.1.Google ScholarGoogle Scholar
  15. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi. 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition. 779--788.Google ScholarGoogle Scholar
  16. B. Schäufele, O. Sawade, D. Becker, and I. Radusch. 2017. A transmission protocol for fully automated valet parking using DSRC. In Consumer Communications & Networking Conference (CCNC), 2017 14th IEEE Annual. IEEE, 636--637.Google ScholarGoogle Scholar
  17. T. Wiegand, G. Sullivan, G. Bjøntegaard, and A. Luthra. 2003. Overview of the H.264/AVC video coding standard. IEEE Transactions on Circuits and Systems for Video Technology 13, 7 (2003), 560--576. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Y. Wu, J. Lim, and M. Yang. 2015. Object tracking benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence 37, 9 (2015), 1834--1848.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. W. Zeng, J. Du, W. Gao, and Q. Huang. 2005. Robust moving object segmentation on H.264/AVC compressed video using the block-based MRF model. Real-Time Imaging 11, 4 (2005), 290--299. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Visual object tracking in a parking garage using compressed domain analysis

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      MMSys '18: Proceedings of the 9th ACM Multimedia Systems Conference
      June 2018
      604 pages
      ISBN:9781450351928
      DOI:10.1145/3204949
      • General Chair:
      • Pablo Cesar,
      • Program Chairs:
      • Michael Zink,
      • Niall Murray

      Copyright © 2018 Owner/Author

      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 12 June 2018

      Check for updates

      Qualifiers

      • demonstration

      Acceptance Rates

      Overall Acceptance Rate176of530submissions,33%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader