Volume III-3
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 295-302, 2016
https://doi.org/10.5194/isprs-annals-III-3-295-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 295-302, 2016
https://doi.org/10.5194/isprs-annals-III-3-295-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  06 Jun 2016

06 Jun 2016

SIMULTANEOUS DETECTION AND TRACKING OF PEDESTRIAN FROM PANORAMIC LASER SCANNING DATA

Wen Xiao1, Bruno Vallet2, Konrad Schindler3, and Nicolas Paparoditis2 Wen Xiao et al.
  • 1School of Civil Engineering and Geosciences, Newcastle University, UK
  • 2versité Paris-Est, IGN, Lab MATIS, France
  • 3Photogrammetry and Remote Sensing, ETH Zürich, Switzerland

Keywords: Moving object detection, object tracking, pedestrian flow estimation, Lidar

Abstract. Pedestrian traffic flow estimation is essential for public place design and construction planning. Traditional data collection by human investigation is tedious, inefficient and expensive. Panoramic laser scanners, e.g. Velodyne HDL-64E, which scan surroundings repetitively at a high frequency, have been increasingly used for 3D object tracking. In this paper, a simultaneous detection and tracking (SDAT) method is proposed for precise and automatic pedestrian trajectory recovery. First, the dynamic environment is detected using two different methods, Nearest-point and Max-distance. Then, all the points on moving objects are transferred into a space-time (x, y, t) coordinate system. The pedestrian detection and tracking amounts to assign the points belonging to pedestrians into continuous trajectories in space-time. We formulate the point assignment task as an energy function which incorporates the point evidence, trajectory number, pedestrian shape and motion. A low energy trajectory will well explain the point observations, and have plausible trajectory trend and length. The method inherently filters out points from other moving objects and false detections. The energy function is solved by a two-step optimization process: tracklet detection in a short temporal window; and global tracklet association through the whole time span. Results demonstrate that the proposed method can automatically recover the pedestrians trajectories with accurate positions and low false detections and mismatches.