Model-Free (Human) Tracking Based on Ground Truth with Time Delay: A 3D Camera Based Approach for Minimizing Tracking Latency and Increasing Tracking Quality

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 383)

Abstract

Model-free tracking allows tracking of objects without prior knowledge of their characteristics. However, many algorithms require a manual initialization to select the target object(s) and perform only a coarse tracking. This article presents a new hybrid approach that allows combining a new fast, model-free tracking algorithm using 3D cameras with an arbitrary separate, slower tracking method that provides a time-delayed ground truth. In particular, we focus on human tracking human and employ Time-of-Flight cameras for the model-free tracking, based on ground truth provided by (multiple) Kinect cameras. The article describes the setup of the system, the model-free tracking algorithm and presents evaluation results for two different scenarios. Results show a high precision and recall, even with large time-delays of the ground truth of up to 10 s.

Keywords

3D camera Model-free tracking Probability propagation 

References

  1. 1.
    Nicolai, P., Raczkowsky, J., Wörn, H.: Continuous Pre-calculation of human tracking with time-delayed ground-truth—a hybrid approach to minimizing tracking latency by combination of different 3D cameras. In: Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics, pp. 121–130 (2015). ISBN 978-989-758-123-6Google Scholar
  2. 2.
    Okada, R., Shirai, Y., Miura, J.: Tracking a person with 3-D motion by integrating optical flow and depth. In: 4th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 336–341 (2000)Google Scholar
  3. 3.
    Tsutsui, H., Miura, J., Shirai, Y.: Optical flow-based person tracking by multiple cameras. In: International Conference on Multisensor Fusion and Integration for Intelligent Systems, pp. 91–96 (2001)Google Scholar
  4. 4.
    Klappstein, J., et al.: Moving Object Segmentation Using Optical Flow and Depth Information. Lecture Notes in Computer Science: Advances in Image and Video Technology, vol. 5414, pp. 611–623 (2009)Google Scholar
  5. 5.
    Jóźków, G., et al.: Combined matching of 2D and 3D Kinect \(^{\text{TM}}\) data to support indoor mapping and navigation. In: Proceedings of Annual Conference of American Society for Photogrammetry and Remote Sensing (2014)Google Scholar
  6. 6.
    Quigley, M. et al.: ROS: an open source Robot Operating System. In: ICRA ’09, International Conference on Robotics and Automation Workshop on Open Source Software (2009)Google Scholar
  7. 7.
    Bradski, G.R., Pisarevsky, V.: Intel’s computer vision library: applications in calibration, stereo, segmentation, tracking, gesture, face and object recognition. In: CVPR’00, IEEE International Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 796–797 (2000)Google Scholar
  8. 8.
    Rusu, R.B., Cousins, S.: 3D is here: point cloud library (PCL). In: ICRA ’11, International Conference on Robotics and Automation, pp. 1–4 (2011)Google Scholar
  9. 9.
    Moennich, H. et al.: A supervision system for the intuitive usage of a telemanipulated surgical robotic setup. In: ROBIO ’11, IEEE International conference on Robotics and Biomimetics, pp. 449–454 (2011)Google Scholar
  10. 10.
    Beyl, T. et al.: Multi kinect people detection for intuitive and safe human robot cooperation in the operating room. In: ICAR ’13, International Conference on Advanced Robotics, pp. 1–6 (2013)Google Scholar
  11. 11.
    Zivkovic, Z., Heijden, F.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn. Lett. 27, 773–780 (2005)CrossRefGoogle Scholar
  12. 12.
    Sobral, A., Vacavant, A.: A comprehensive review of background subtraction algorithms evaluated with synthetic and real video. In: Computer Vision and Understanding, vol. 122, pp. 4–21. Elsevier (2014)Google Scholar
  13. 13.
    Sanchéz, J., Meinhardt-Llopis, E., Facciolo, G.: TV-L1 Optical Flow Estimation. Image Process. Online 3, 137–150 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute for Anthropomatics and Robotics (IAR), Intelligent Process Control and Robotics Lab (IPR), Karlsruhe Institute of Technology (KIT)KarlsruheGermany

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