Advertisement

Pedestrian Detection and Tracking Using Three-Dimensional LADAR Data

  • Luis E. Navarro-Serment
  • Christoph Mertz
  • Martial Hebert
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 62)

Abstract

The approach investigated in this work employs three-dimensional LADAR measurements to detect and track pedestrians over time. The sensor is employed on a moving vehicle. The algorithm quickly detects the objects which have the potential of being humans using a subset of these points, and then classifies each object using statistical pattern recognition techniques. The algorithm uses geometric and motion features to recognize human signatures. The perceptual capabilities described form the basis for safe and robust navigation in autonomous vehicles, necessary to safeguard pedestrians operating in the vicinity of a moving robotic vehicle.

Keywords

Point Cloud Autonomous Vehicle Human Detection Pedestrian Detection Unmanned Ground Vehicle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Arras, K.O., Mozos, O.M., Burgard, W.: Using Boosted Features for the Detection of People in 2D Range Data. In: Proc. of the 2007 IEEE Int. Conf. on Robotics and Automation, Roma, Italy, April 10-14, pp. 3402–3407 (2007)Google Scholar
  2. 2.
    Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. In: Data Mining and Knowledge Discovery, vol. 2, pp. 121–167. Kluwer Academic Pub., Boston (1998)Google Scholar
  3. 3.
    Howard, A., Matthies, L.H., Huertas, A., Bajracharya, M., Rankin, A.: Detecting Pedestrians with Stereo Vision: Safe Operation of Autonomous Ground Vehicles in Dynamic Environments. In: Proc. of the 13th. International Symposium of Robotics Research, November 26-29 (2007)Google Scholar
  4. 4.
    Morris, D., Colonna, B., Haley, P.: Ladar-based Mover Detection from Moving Vehicles. In: Proc. of the 25th Army Science Conference (November 2006)Google Scholar
  5. 5.
    Navarro-Serment, L.E., Mertz, C., Hebert, M.: Predictive Mover Detection and Tracking in Cluttered Environments. In: Proc. of the 25th. Army Science Conference, November 27-30 (2006)Google Scholar
  6. 6.
    Navarro-Serment, L.E., Mertz, C., Vandapel, N., Hebert, M.: LADAR-based Pedestrian Detection and Tracking. In: IEEE Workshop on Human Detection from Mobile Platforms, Pasadena, California, May 20 (2008)Google Scholar
  7. 7.
    Shoemaker, C.M., Bornstein, J.A.: The Demo III UGV Program: a Testbed for Autonomous Navigation Research. In: Proc. of the IEEE Int. Symposium on Intelligent Control, Gaithersburg, MD, September 1998, pp. 644–651 (1998)Google Scholar
  8. 8.
    Thornton, S., Hoffelder, M., Morris, D.: Multi-sensor Detection and Tracking of Humans for Safe Operations with Unmanned Ground Vehicles. In: 1st. IEEE Workshop on Human Detection from Mobile Platforms, Pasadena, California, May 20 (2008)Google Scholar
  9. 9.
    Thornton, S., Patil, R.: Robust Detection of Humans Using Multi-sensor Features. In: Proc. of the 26th. Army Science Conference, December 1-4 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Luis E. Navarro-Serment
    • 1
  • Christoph Mertz
    • 1
  • Martial Hebert
    • 1
  1. 1.The Robotics InstituteCarnegie Mellon UniversityPittsburgh

Personalised recommendations