Advertisement

Multiple People Tracking Using Moment Based Approach

  • Sachin Kansal
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)

Abstract

This paper has the capability to detect multiple people in indoor and outdoor environment. In this paper we have used single camera. In this paper we proposed a technique in which it performs multiple face detection, from this it extracts the people’s torso regions and stores the HSV range of each person. After this when person’s face is not in front of the camera it will track all those people’s using moment based approach i.e. it will compute the area of exposed torso region and centre of gravity of the segmented torso region of each person. In this paper we consider torso region HSV range as the key feature. From this we calculate the tracking parameters for each person. In this paper speech thread module is implemented to have interaction with the system. Experiment results validate the robust performance of the proposed approach.

Keywords

Face Detection HSV Range Tracking and following Color features Moment Calculation Speech Generation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Song, K.T., Chen, W.J.: Face recognition and tracking for human-robot interaction. IEEE International Conferences on Systems, Man and Cybernetics, The Hague, Netherlands 3, 2877–2882 (2004)Google Scholar
  2. 2.
    Kwon, H., Yoon, Y., Park, J.B., Kak, A.C.: Person tracking with a mobile robot using two uncalibrated independently moving cameras. In: IEEE International Conferences on Robotics and Automation, Barcelona, Spain, pp. 2877–2883 (2005)Google Scholar
  3. 3.
    Food, A., Howard, A., Mataric, M.J.: Laser based people tracking. In: Proceedings of the IEEE International Conferences on Robotics & Automation (ICRA), Washington, DC, United States, pp. 3024–3029 (2002)Google Scholar
  4. 4.
    Montemerlo, M., Thun, S., Whittaker, W.: Conditional particle filters for simultaneous mobile robot localization and people tracking. In: Proceedings of the IEEE International Conference on Robotics & Automation (ICRA), Washington, DC, USA, pp. 695–701 (2002)Google Scholar
  5. 5.
    Shin, J.-H., Kim, W., Lee, J.-J.: Real-time object tracking and segmentation using adaptive color snake model. International Journal of Control, Automation, and Systems 4(2), 236–246 (2006)Google Scholar
  6. 6.
    Scheutz, M., McRaven, J., Cserey, G.: Fast, reliable, adaptive, bimodal people tracking for indoor environments. In: Proc. of the 2004 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS 2004), Sendai, Japan, vol. 2, pp. 1347–1352 (2004)Google Scholar
  7. 7.
    Fritsch, J., Kleinehagenbrock, M., Lang, S., Fink, G.A., Sagerer, G.: Audiovisual person tracking with a mobile robot. In: Proceedings of International Conference on Intelligent Autonomous Systems, pp. 898–906 (2004)Google Scholar
  8. 8.
    Kansal, S., Chakraborty, P.: Tracking of Person Using monocular Vision by Autonomous Navigation Test bed (ANT). International Journal of Applied Information Systems (IJAIS) 3(9) (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Indian Institute of Technology DelhiDelhiIndia

Personalised recommendations